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vision</title> <meta name="description" content="Search results for: machine vision"> <meta name="keywords" content="machine vision"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="machine vision"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3796</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: machine vision</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3796</span> Optimizing Machine Vision System Setup Accuracy by Six-Sigma DMAIC Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20C.%20Chen">Joseph C. Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine vision system provides automatic inspection to reduce manufacturing costs considerably. However, only a few principles have been found to optimize machine vision system and help it function more accurately in industrial practice. Mostly, there were complicated and impractical design techniques to improve the accuracy of machine vision system. This paper discusses implementing the Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) approach to optimize the setup parameters of machine vision system when it is used as a direct measurement technique. This research follows a case study showing how Six Sigma DMAIC methodology has been put into use. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DMAIC" title="DMAIC">DMAIC</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision%20system" title=" machine vision system"> machine vision system</a>, <a href="https://publications.waset.org/abstracts/search?q=process%20capability" title=" process capability"> process capability</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20Parameter%20Design" title=" Taguchi Parameter Design"> Taguchi Parameter Design</a> </p> <a href="https://publications.waset.org/abstracts/68243/optimizing-machine-vision-system-setup-accuracy-by-six-sigma-dmaic-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68243.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">436</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3795</span> The Role of Synthetic Data in Aerial Object Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ava%20Dodd">Ava Dodd</a>, <a href="https://publications.waset.org/abstracts/search?q=Jonathan%20Adams"> Jonathan Adams</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data" title=" synthetic data"> synthetic data</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOv4" title=" YOLOv4"> YOLOv4</a> </p> <a href="https://publications.waset.org/abstracts/139194/the-role-of-synthetic-data-in-aerial-object-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139194.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">225</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3794</span> Development of Agricultural Robotic Platform for Inter-Row Plant: An Autonomous Navigation Based on Machine Vision </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alaa%20El-Din%20Rezk">Alaa El-Din Rezk </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Egypt, management of crops still away from what is being used today by utilizing the advances of mechanical design capabilities, sensing and electronics technology. These technologies have been introduced in many places and recorm, for Straight Path, Curved Path, Sine Wave ded high accuracy in different field operations. So, an autonomous robotic platform based on machine vision has been developed and constructed to be implemented in Egyptian conditions as self-propelled mobile vehicle for carrying tools for inter/intra-row crop management based on different control modules. The experiments were carried out at plant protection research institute (PPRI) during 2014-2015 to optimize the accuracy of agricultural robotic platform control using machine vision in term of the autonomous navigation and performance of the robot’s guidance system. Results showed that the robotic platform' guidance system with machine vision was able to adequately distinguish the path and resisted image noise and did better than human operators for getting less lateral offset error. The average error of autonomous was 2.75, 19.33, 21.22, 34.18, and 16.69 mm. while the human operator was 32.70, 4.85, 7.85, 38.35 and 14.75 mm Path, Offset Discontinuity and Angle Discontinuity respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20robotic" title="autonomous robotic">autonomous robotic</a>, <a href="https://publications.waset.org/abstracts/search?q=Hough%20transform" title=" Hough transform"> Hough transform</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision "> machine vision </a> </p> <a href="https://publications.waset.org/abstracts/43565/development-of-agricultural-robotic-platform-for-inter-row-plant-an-autonomous-navigation-based-on-machine-vision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43565.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">315</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3793</span> Framework for Socio-Technical Issues in Requirements Engineering for Developing Resilient Machine Vision Systems Using Levels of Automation through the Lifecycle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ryan%20Messina">Ryan Messina</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehedi%20Hasan"> Mehedi Hasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research is to examine the impacts of using data to generate performance requirements for automation in visual inspections using machine vision. These situations are intended for design and how projects can smooth the transfer of tacit knowledge to using an algorithm. We have proposed a framework when specifying machine vision systems. This framework utilizes varying levels of automation as contingency planning to reduce data processing complexity. Using data assists in extracting tacit knowledge from those who can perform the manual tasks to assist design the system; this means that real data from the system is always referenced and minimizes errors between participating parties. We propose using three indicators to know if the project has a high risk of failing to meet requirements related to accuracy and reliability. All systems tested achieved a better integration into operations after applying the framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automation" title="automation">automation</a>, <a href="https://publications.waset.org/abstracts/search?q=contingency%20planning" title=" contingency planning"> contingency planning</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous%20engineering" title=" continuous engineering"> continuous engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20theory" title=" control theory"> control theory</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20requirements" title=" system requirements"> system requirements</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20thinking" title=" system thinking"> system thinking</a> </p> <a href="https://publications.waset.org/abstracts/97643/framework-for-socio-technical-issues-in-requirements-engineering-for-developing-resilient-machine-vision-systems-using-levels-of-automation-through-the-lifecycle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97643.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">204</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3792</span> Alternative Approach to the Machine Vision System Operating for Solving Industrial Control Issue</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20Nikitenko">M. S. Nikitenko</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Kizilov"> S. A. Kizilov</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Y.%20Khudonogov"> D. Y. Khudonogov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper considers an approach to a machine vision operating system combined with using a grid of light markers. This approach is used to solve several scientific and technical problems, such as measuring the capability of an apron feeder delivering coal from a lining return port to a conveyor in the technology of mining high coal releasing to a conveyor and prototyping an autonomous vehicle obstacle detection system. Primary verification of a method of calculating bulk material volume using three-dimensional modeling and validation in laboratory conditions with relative errors calculation were carried out. A method of calculating the capability of an apron feeder based on a machine vision system and a simplifying technology of a three-dimensional modelled examined measuring area with machine vision was offered. The proposed method allows measuring the volume of rock mass moved by an apron feeder using machine vision. This approach solves the volume control issue of coal produced by a feeder while working off high coal by lava complexes with release to a conveyor with accuracy applied for practical application. The developed mathematical apparatus for measuring feeder productivity in kg/s uses only basic mathematical functions such as addition, subtraction, multiplication, and division. Thus, this fact simplifies software development, and this fact expands the variety of microcontrollers and microcomputers suitable for performing tasks of calculating feeder capability. A feature of an obstacle detection issue is to correct distortions of the laser grid, which simplifies their detection. The paper presents algorithms for video camera image processing and autonomous vehicle model control based on obstacle detection machine vision systems. A sample fragment of obstacle detection at the moment of distortion with the laser grid is demonstrated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title="machine vision">machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision%20operating%20system" title=" machine vision operating system"> machine vision operating system</a>, <a href="https://publications.waset.org/abstracts/search?q=light%20markers" title=" light markers"> light markers</a>, <a href="https://publications.waset.org/abstracts/search?q=measuring%20capability" title=" measuring capability"> measuring capability</a>, <a href="https://publications.waset.org/abstracts/search?q=obstacle%20detection%20system" title=" obstacle detection system"> obstacle detection system</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20transport" title=" autonomous transport"> autonomous transport</a> </p> <a href="https://publications.waset.org/abstracts/159442/alternative-approach-to-the-machine-vision-system-operating-for-solving-industrial-control-issue" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159442.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">114</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3791</span> Machine Vision System for Measuring the Quality of Bulk Sun-dried Organic Raisins</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Navab%20Karimi">Navab Karimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tohid%20Alizadeh"> Tohid Alizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An intelligent vision-based system was designed to measure the quality and purity of raisins. A machine vision setup was utilized to capture the images of bulk raisins in ranges of 5-50% mixed pure-impure berries. The textural features of bulk raisins were extracted using Grey-level Histograms, Co-occurrence Matrix, and Local Binary Pattern (a total of 108 features). Genetic Algorithm and neural network regression were used for selecting and ranking the best features (21 features). As a result, the GLCM features set was found to have the highest accuracy (92.4%) among the other sets. Followingly, multiple feature combinations of the previous stage were fed into the second regression (linear regression) to increase accuracy, wherein a combination of 16 features was found to be the optimum. Finally, a Support Vector Machine (SVM) classifier was used to differentiate the mixtures, producing the best efficiency and accuracy of 96.2% and 97.35%, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sun-dried%20organic%20raisin" title="sun-dried organic raisin">sun-dried organic raisin</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=ann%20regression" title=" ann regression"> ann regression</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression" title=" linear regression"> linear regression</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=south%20azerbaijan." title=" south azerbaijan."> south azerbaijan.</a> </p> <a href="https://publications.waset.org/abstracts/172004/machine-vision-system-for-measuring-the-quality-of-bulk-sun-dried-organic-raisins" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172004.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">73</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3790</span> A Combined Approach Based on Artificial Intelligence and Computer Vision for Qualitative Grading of Rice Grains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hemad%20Zareiforoush">Hemad Zareiforoush</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Minaei"> Saeed Minaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Banakar"> Ahmad Banakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Reza%20Alizadeh"> Mohammad Reza Alizadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quality inspection of rice (Oryza sativa L.) during its various processing stages is very important. In this research, an artificial intelligence-based model coupled with computer vision techniques was developed as a decision support system for qualitative grading of rice grains. For conducting the experiments, first, 25 samples of rice grains with different levels of percentage of broken kernels (PBK) and degree of milling (DOM) were prepared and their qualitative grade was assessed by experienced experts. Then, the quality parameters of the same samples examined by experts were determined using a machine vision system. A grading model was developed based on fuzzy logic theory in MATLAB software for making a relationship between the qualitative characteristics of the product and its quality. Totally, 25 rules were used for qualitative grading based on AND operator and Mamdani inference system. The fuzzy inference system was consisted of two input linguistic variables namely, DOM and PBK, which were obtained by the machine vision system, and one output variable (quality of the product). The model output was finally defuzzified using Center of Maximum (COM) method. In order to evaluate the developed model, the output of the fuzzy system was compared with experts’ assessments. It was revealed that the developed model can estimate the qualitative grade of the product with an accuracy of 95.74%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title="machine vision">machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=rice" title=" rice"> rice</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a> </p> <a href="https://publications.waset.org/abstracts/9943/a-combined-approach-based-on-artificial-intelligence-and-computer-vision-for-qualitative-grading-of-rice-grains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9943.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">419</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3789</span> Paddy/Rice Singulation for Determination of Husking Efficiency and Damage Using Machine Vision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Shaker">M. Shaker</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Minaei"> S. Minaei</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Khoshtaghaza"> M. H. Khoshtaghaza</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Banakar"> A. Banakar</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Jafari"> A. Jafari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study a system of machine vision and singulation was developed to separate paddy from rice and determine paddy husking and rice breakage percentages. The machine vision system consists of three main components including an imaging chamber, a digital camera, a computer equipped with image processing software. The singulation device consists of a kernel holding surface, a motor with vacuum fan, and a dimmer. For separation of paddy from rice (in the image), it was necessary to set a threshold. Therefore, some images of paddy and rice were sampled and the RGB values of the images were extracted using MATLAB software. Then mean and standard deviation of the data were determined. An Image processing algorithm was developed using MATLAB to determine paddy/rice separation and rice breakage and paddy husking percentages, using blue to red ratio. Tests showed that, a threshold of 0.75 is suitable for separating paddy from rice kernels. Results from the evaluation of the image processing algorithm showed that the accuracies obtained with the algorithm were 98.36% and 91.81% for paddy husking and rice breakage percentage, respectively. Analysis also showed that a suction of 45 mmHg to 50 mmHg yielding 81.3% separation efficiency is appropriate for operation of the kernel singulation system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breakage" title="breakage">breakage</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=husking" title=" husking"> husking</a>, <a href="https://publications.waset.org/abstracts/search?q=rice%20kernel" title=" rice kernel"> rice kernel</a> </p> <a href="https://publications.waset.org/abstracts/52408/paddyrice-singulation-for-determination-of-husking-efficiency-and-damage-using-machine-vision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52408.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">381</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3788</span> Image Processing techniques for Surveillance in Outdoor Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jayanth%20C.">Jayanth C.</a>, <a href="https://publications.waset.org/abstracts/search?q=Anirudh%20Sai%20Yetikuri"> Anirudh Sai Yetikuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Kavitha%20S.%20N."> Kavitha S. N.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explores the development and application of computer vision and machine learning techniques for real-time pose detection, facial recognition, and number plate extraction. Utilizing MediaPipe for pose estimation, the research presents methods for detecting hand raises and ducking postures through real-time video analysis. Complementarily, facial recognition is employed to compare and verify individual identities using the face recognition library. Additionally, the paper demonstrates a robust approach for extracting and storing vehicle number plates from images, integrating Optical Character Recognition (OCR) with a database management system. The study highlights the effectiveness and versatility of these technologies in practical scenarios, including security and surveillance applications. The findings underscore the potential of combining computer vision techniques to address diverse challenges and enhance automated systems for both individual and vehicular identification. This research contributes to the fields of computer vision and machine learning by providing scalable solutions and demonstrating their applicability in real-world contexts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20detection" title=" pose detection"> pose detection</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20recognition" title=" facial recognition"> facial recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=number%20plate%20extraction" title=" number plate extraction"> number plate extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20analysis" title=" real-time analysis"> real-time analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=OCR" title=" OCR"> OCR</a>, <a href="https://publications.waset.org/abstracts/search?q=database%20management" title=" database management"> database management</a> </p> <a href="https://publications.waset.org/abstracts/191153/image-processing-techniques-for-surveillance-in-outdoor-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191153.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">26</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3787</span> An Investigation on Smartphone-Based Machine Vision System for Inspection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=They%20Shao%20Peng">They Shao Peng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine vision system for inspection is an automated technology that is normally utilized to analyze items on the production line for quality control purposes, it also can be known as an automated visual inspection (AVI) system. By applying automated visual inspection, the existence of items, defects, contaminants, flaws, and other irregularities in manufactured products can be easily detected in a short time and accurately. However, AVI systems are still inflexible and expensive due to their uniqueness for a specific task and consuming a lot of set-up time and space. With the rapid development of mobile devices, smartphones can be an alternative device for the visual system to solve the existing problems of AVI. Since the smartphone-based AVI system is still at a nascent stage, this led to the motivation to investigate the smartphone-based AVI system. This study is aimed to provide a low-cost AVI system with high efficiency and flexibility. In this project, the object detection models, which are You Only Look Once (YOLO) model and Single Shot MultiBox Detector (SSD) model, are trained, evaluated, and integrated with the smartphone and webcam devices. The performance of the smartphone-based AVI is compared with the webcam-based AVI according to the precision and inference time in this study. Additionally, a mobile application is developed which allows users to implement real-time object detection and object detection from image storage. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20visual%20inspection" title="automated visual inspection">automated visual inspection</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20application" title=" mobile application"> mobile application</a> </p> <a href="https://publications.waset.org/abstracts/151908/an-investigation-on-smartphone-based-machine-vision-system-for-inspection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">123</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3786</span> Artificial Intelligence and Machine Vision-Based Defect Detection Methodology for Solid Rocket Motor Propellant Grains</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sandip%20Suman">Sandip Suman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mechanical defects (cracks, voids, irregularities) in rocket motor propellant are not new and it is induced due to various reasons, which could be an improper manufacturing process, lot-to-lot variation in chemicals or just the natural aging of the products. These defects are normally identified during the examination of radiographic films by quality inspectors. However, a lot of times, these defects are under or over-classified by human inspectors, which leads to unpredictable performance during lot acceptance tests and significant economic loss. The human eye can only visualize larger cracks and defects in the radiographs, and it is almost impossible to visualize every small defect through the human eye. A different artificial intelligence-based machine vision methodology has been proposed in this work to identify and classify the structural defects in the radiographic films of rocket motors with solid propellant. The proposed methodology can extract the features of defects, characterize them, and make intelligent decisions for acceptance or rejection as per the customer requirements. This will automatize the defect detection process during manufacturing with human-like intelligence. It will also significantly reduce production downtime and help to restore processes in the least possible time. The proposed methodology is highly scalable and can easily be transferred to various products and processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=defect%20detection" title=" defect detection"> defect detection</a>, <a href="https://publications.waset.org/abstracts/search?q=rocket%20motor%20propellant%20grains" title=" rocket motor propellant grains"> rocket motor propellant grains</a> </p> <a href="https://publications.waset.org/abstracts/168782/artificial-intelligence-and-machine-vision-based-defect-detection-methodology-for-solid-rocket-motor-propellant-grains" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168782.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">98</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3785</span> Machine Learning Strategies for Data Extraction from Unstructured Documents in Financial Services</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Delphine%20Vendryes">Delphine Vendryes</a>, <a href="https://publications.waset.org/abstracts/search?q=Dushyanth%20Sekhar"> Dushyanth Sekhar</a>, <a href="https://publications.waset.org/abstracts/search?q=Baojia%20Tong"> Baojia Tong</a>, <a href="https://publications.waset.org/abstracts/search?q=Matthew%20Theisen"> Matthew Theisen</a>, <a href="https://publications.waset.org/abstracts/search?q=Chester%20Curme"> Chester Curme</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=entity%20recognition" title=" entity recognition"> entity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=finance" title=" finance"> finance</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title=" information retrieval"> information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a> </p> <a href="https://publications.waset.org/abstracts/126688/machine-learning-strategies-for-data-extraction-from-unstructured-documents-in-financial-services" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/126688.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3784</span> Visual Improvement with Low Vision Aids in Children with Stargardt’s Disease</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anum%20Akhter">Anum Akhter</a>, <a href="https://publications.waset.org/abstracts/search?q=Sumaira%20Altaf"> Sumaira Altaf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: To study the effect of low vision devices i.e. telescope and magnifying glasses on distance visual acuity and near visual acuity of children with Stargardt’s disease. Setting: Low vision department, Alshifa Trust Eye Hospital, Rawalpindi, Pakistan. Methods: 52 children having Stargardt’s disease were included in the study. All children were diagnosed by pediatrics ophthalmologists. Comprehensive low vision assessment was done by me in Low vision clinic. Visual acuity was measured using ETDRS chart. Refraction and other supplementary tests were performed. Children with Stargardt’s disease were provided with different telescopes and magnifying glasses for improving far vision and near vision. Results: Out of 52 children, 17 children were males and 35 children were females. Distance visual acuity and near visual acuity improved significantly with low vision aid trial. All children showed visual acuity better than 6/19 with a telescope of higher magnification. Improvement in near visual acuity was also significant with magnifying glasses trial. Conclusions: Low vision aids are useful for improvement in visual acuity in children. Children with Stargardt’s disease who are having a problem in education and daily life activities can get help from low vision aids. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Stargardt" title="Stargardt">Stargardt</a>, <a href="https://publications.waset.org/abstracts/search?q=s%20disease" title="s disease">s disease</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20vision%20aids" title=" low vision aids"> low vision aids</a>, <a href="https://publications.waset.org/abstracts/search?q=telescope" title=" telescope"> telescope</a>, <a href="https://publications.waset.org/abstracts/search?q=magnifiers" title=" magnifiers"> magnifiers</a> </p> <a href="https://publications.waset.org/abstracts/24382/visual-improvement-with-low-vision-aids-in-children-with-stargardts-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24382.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">538</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3783</span> A Comparison of YOLO Family for Apple Detection and Counting in Orchards</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuanqing%20Li">Yuanqing Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Changyi%20Lei"> Changyi Lei</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhaopeng%20Xue"> Zhaopeng Xue</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhuo%20Zheng"> Zhuo Zheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Yanbo%20Long"> Yanbo Long</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agricultural%20object%20detection" title="agricultural object detection">agricultural object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLO%20family" title=" YOLO family"> YOLO family</a> </p> <a href="https://publications.waset.org/abstracts/134964/a-comparison-of-yolo-family-for-apple-detection-and-counting-in-orchards" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134964.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">197</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3782</span> A Review: Detection and Classification Defects on Banana and Apples by Computer Vision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahow%20Muoftah">Zahow Muoftah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=banana" title=" banana"> banana</a>, <a href="https://publications.waset.org/abstracts/search?q=apple" title=" apple"> apple</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/154514/a-review-detection-and-classification-defects-on-banana-and-apples-by-computer-vision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154514.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3781</span> Autonomous Kuka Youbot Navigation Based on Machine Learning and Path Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Gordon">Carlos Gordon</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricio%20Encalada"> Patricio Encalada</a>, <a href="https://publications.waset.org/abstracts/search?q=Henry%20Lema"> Henry Lema</a>, <a href="https://publications.waset.org/abstracts/search?q=Diego%20Leon"> Diego Leon</a>, <a href="https://publications.waset.org/abstracts/search?q=Dennis%20Chicaiza"> Dennis Chicaiza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The following work presents a proposal of autonomous navigation of mobile robots implemented in an omnidirectional robot Kuka Youbot. We have been able to perform the integration of robotic operative system (ROS) and machine learning algorithms. ROS mainly provides two distributions; ROS hydro and ROS Kinect. ROS hydro allows managing the nodes of odometry, kinematics, and path planning with statistical and probabilistic, global and local algorithms based on Adaptive Monte Carlo Localization (AMCL) and Dijkstra. Meanwhile, ROS Kinect is responsible for the detection block of dynamic objects which can be in the points of the planned trajectory obstructing the path of Kuka Youbot. The detection is managed by artificial vision module under a trained neural network based on the single shot multibox detector system (SSD), where the main dynamic objects for detection are human beings and domestic animals among other objects. When the objects are detected, the system modifies the trajectory or wait for the decision of the dynamic obstacle. Finally, the obstacles are skipped from the planned trajectory, and the Kuka Youbot can reach its goal thanks to the machine learning algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20navigation" title="autonomous navigation">autonomous navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=path%20planning" title=" path planning"> path planning</a>, <a href="https://publications.waset.org/abstracts/search?q=robotic%20operative%20system" title=" robotic operative system"> robotic operative system</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source%20computer%20vision%20library" title=" open source computer vision library"> open source computer vision library</a> </p> <a href="https://publications.waset.org/abstracts/101726/autonomous-kuka-youbot-navigation-based-on-machine-learning-and-path-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101726.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3780</span> Vision Based People Tracking System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boukerch%20Haroun">Boukerch Haroun</a>, <a href="https://publications.waset.org/abstracts/search?q=Luo%20Qing%20Sheng"> Luo Qing Sheng</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Hua%20Shi"> Li Hua Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Boukraa%20Sebti"> Boukraa Sebti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present the design and the implementation of a target tracking system where the target is set to be a moving person in a video sequence. The system can be applied easily as a vision system for mobile robot. The system is composed of two major parts the first is the detection of the person in the video frame using the SVM learning machine based on the “HOG” descriptors. The second part is the tracking of a moving person it’s done by using a combination of the Kalman filter and a modified version of the Camshift tracking algorithm by adding the target motion feature to the color feature, the experimental results had shown that the new algorithm had overcame the traditional Camshift algorithm in robustness and in case of occlusion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camshift%20algorithm" title="camshift algorithm">camshift algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filter" title=" Kalman filter"> Kalman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title=" object tracking"> object tracking</a> </p> <a href="https://publications.waset.org/abstracts/2264/vision-based-people-tracking-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2264.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">446</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3779</span> An Evaluation of Neural Network Efficacies for Image Recognition on Edge-AI Computer Vision Platform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jie%20Zhao">Jie Zhao</a>, <a href="https://publications.waset.org/abstracts/search?q=Meng%20Su"> Meng Su</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alexNet" title="alexNet">alexNet</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG" title=" VGG"> VGG</a>, <a href="https://publications.waset.org/abstracts/search?q=googleNet" title=" googleNet"> googleNet</a>, <a href="https://publications.waset.org/abstracts/search?q=resNet" title=" resNet"> resNet</a>, <a href="https://publications.waset.org/abstracts/search?q=Jetson%20nano" title=" Jetson nano"> Jetson nano</a>, <a href="https://publications.waset.org/abstracts/search?q=CUDA" title=" CUDA"> CUDA</a>, <a href="https://publications.waset.org/abstracts/search?q=COCO-NET" title=" COCO-NET"> COCO-NET</a>, <a href="https://publications.waset.org/abstracts/search?q=cifar10" title=" cifar10"> cifar10</a>, <a href="https://publications.waset.org/abstracts/search?q=imageNet%20large%20scale%20visual%20recognition%20challenge%20%28ILSVRC%29" title=" imageNet large scale visual recognition challenge (ILSVRC)"> imageNet large scale visual recognition challenge (ILSVRC)</a>, <a href="https://publications.waset.org/abstracts/search?q=google%20colab" title=" google colab"> google colab</a> </p> <a href="https://publications.waset.org/abstracts/176759/an-evaluation-of-neural-network-efficacies-for-image-recognition-on-edge-ai-computer-vision-platform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176759.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">90</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3778</span> An Intelligent Baby Care System Based on IoT and Deep Learning Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinlun%20Lai">Chinlun Lai</a>, <a href="https://publications.waset.org/abstracts/search?q=Lunjyh%20Jiang"> Lunjyh Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=baby%20care%20system" title="baby care system">baby care system</a>, <a href="https://publications.waset.org/abstracts/search?q=Internet%20of%20Things" title=" Internet of Things"> Internet of Things</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a> </p> <a href="https://publications.waset.org/abstracts/78003/an-intelligent-baby-care-system-based-on-iot-and-deep-learning-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">224</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3777</span> 3D Human Reconstruction over Cloud Based Image Data via AI and Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kaushik%20Sathupadi">Kaushik Sathupadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandesh%20Achar"> Sandesh Achar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20analysis" title=" human motion analysis"> human motion analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/190028/3d-human-reconstruction-over-cloud-based-image-data-via-ai-and-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190028.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">36</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3776</span> Image Multi-Feature Analysis by Principal Component Analysis for Visual Surface Roughness Measurement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei%20Zhang">Wei Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20He"> Yan He</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Wang"> Yan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yufeng%20Li"> Yufeng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Chuanpeng%20Hao"> Chuanpeng Hao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Surface roughness is an important index for evaluating surface quality, needs to be accurately measured to ensure the performance of the workpiece. The roughness measurement based on machine vision involves various image features, some of which are redundant. These redundant features affect the accuracy and speed of the visual approach. Previous research used correlation analysis methods to select the appropriate features. However, this feature analysis is independent and cannot fully utilize the information of data. Besides, blindly reducing features lose a lot of useful information, resulting in unreliable results. Therefore, the focus of this paper is on providing a redundant feature removal approach for visual roughness measurement. In this paper, the statistical methods and gray-level co-occurrence matrix(GLCM) are employed to extract the texture features of machined images effectively. Then, the principal component analysis(PCA) is used to fuse all extracted features into a new one, which reduces the feature dimension and maintains the integrity of the original information. Finally, the relationship between new features and roughness is established by the support vector machine(SVM). The experimental results show that the approach can effectively solve multi-feature information redundancy of machined surface images and provides a new idea for the visual evaluation of surface roughness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20analysis" title="feature analysis">feature analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20roughness" title=" surface roughness"> surface roughness</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/138525/image-multi-feature-analysis-by-principal-component-analysis-for-visual-surface-roughness-measurement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138525.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">212</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3775</span> Support Vector Machine Based Retinal Therapeutic for Glaucoma Using Machine Learning Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingmin%20Pan"> Mingmin Pan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Yung"> Yang Yung</a>, <a href="https://publications.waset.org/abstracts/search?q=Tracy%20Lin%20Huan"> Tracy Lin Huan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20algorithm" title="machine learning algorithm">machine learning algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20clustering%20mode" title=" correlation clustering mode"> correlation clustering mode</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20overlapping%20system" title=" cluster overlapping system"> cluster overlapping system</a>, <a href="https://publications.waset.org/abstracts/search?q=glaucoma" title=" glaucoma"> glaucoma</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20density%20estimation" title=" kernel density estimation"> kernel density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20therapeutic" title=" retinal therapeutic"> retinal therapeutic</a> </p> <a href="https://publications.waset.org/abstracts/80153/support-vector-machine-based-retinal-therapeutic-for-glaucoma-using-machine-learning-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80153.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">254</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3774</span> An Investigation into Computer Vision Methods to Identify Material Other Than Grapes in Harvested Wine Grape Loads</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Riaan%20Kleyn">Riaan Kleyn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilize mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar which degrades the quality of wine that can be produced. Currently, wine cellars do not have a method to determine the amount of MOG present within wine grape loads. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=wine%20grapes" title=" wine grapes"> wine grapes</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20harvested%20grapes" title=" machine harvested grapes"> machine harvested grapes</a> </p> <a href="https://publications.waset.org/abstracts/169700/an-investigation-into-computer-vision-methods-to-identify-material-other-than-grapes-in-harvested-wine-grape-loads" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169700.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3773</span> FLIME - Fast Low Light Image Enhancement for Real-Time Video</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinay%20P.">Vinay P.</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20K.%20S."> Srinivas K. S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low%20light%20image%20enhancement" title="low light image enhancement">low light image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20video" title=" real-time video"> real-time video</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/144526/flime-fast-low-light-image-enhancement-for-real-time-video" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144526.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">204</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3772</span> A Systematic Categorization of Arguments against the Vision Zero Goal: A Literature Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Henok%20Girma%20Abebe">Henok Girma Abebe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Vision Zero is a long-term goal of preventing all road traffic fatalities and serious injuries which was first adopted in Sweden in 1997. It is based on the assumption that death and serious injury in the road system is morally unacceptable. In order to approach this end, vision zero has put in place strategies that are radically different from the traditional safety work. The vision zero, for instance, promoted the adoption of the best available technology to promote safety, and placed the ultimate responsibility for traffic safety on system designers. Despite Vision Zero’s moral appeal and its expansion to different safety areas and also parts of the world, important philosophical concerns related to the adoption and implementation of the vision zero remain to be addressed. Moreover, the vision zero goal has been criticized on different grounds. The aim of this paper is to identify and systematically categorize criticisms that have been put forward against vision zero. The findings of the paper are solely based on a critical analysis of secondary sources and snowball method is employed to identify the relevant philosophical and empirical literatures. Two general categories of criticisms on the vision zero goal are identified. The first category consists of criticisms that target the setting of vision zero as a ‘goal’ and some of the basic assumptions upon which the goal is based. Among others, the goal of achieving zero fatalities and serious injuries, together with vision zero’s lexicographical prioritization of safety has been criticized as unrealistic. The second category consists of criticisms that target the strategies put in place to achieve the goal of zero fatalities and serious injuries. For instance, Vision zero’s responsibility ascription for road safety and its rejection of cost-benefit analysis in the formulation and adoption of safety measures has both been criticized as counterproductive. In this category also falls the criticism that Vision Zero safety measures tend to be too paternalistic. Significant improvements have been recorded in road safety work since the adoption of vision zero, however, for the vision zero to even succeed more, it is important that issues and criticisms of philosophical nature associated with it are identified and critically dealt with. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=criticisms" title="criticisms">criticisms</a>, <a href="https://publications.waset.org/abstracts/search?q=systems%20approach" title=" systems approach"> systems approach</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20safety" title=" traffic safety"> traffic safety</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20zero" title=" vision zero"> vision zero</a> </p> <a href="https://publications.waset.org/abstracts/97167/a-systematic-categorization-of-arguments-against-the-vision-zero-goal-a-literature-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97167.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">301</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3771</span> A Biologically Inspired Approach to Automatic Classification of Textile Fabric Prints Based On Both Texture and Colour Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babar%20Khan">Babar Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Zhijie"> Wang Zhijie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine Vision has been playing a significant role in Industrial Automation, to imitate the wide variety of human functions, providing improved safety, reduced labour cost, the elimination of human error and/or subjective judgments, and the creation of timely statistical product data. Despite the intensive research, there have not been any attempts to classify fabric prints based on printed texture and colour, most of the researches so far encompasses only black and white or grey scale images. We proposed a biologically inspired processing architecture to classify fabrics w.r.t. the fabric print texture and colour. We created a texture descriptor based on the HMAX model for machine vision, and incorporated colour descriptor based on opponent colour channels simulating the single opponent and double opponent neuronal function of the brain. We found that our algorithm not only outperformed the original HMAX algorithm on classification of fabric print texture and colour, but we also achieved a recognition accuracy of 85-100% on different colour and different texture fabric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20classification" title="automatic classification">automatic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20descriptor" title=" texture descriptor"> texture descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=colour%20descriptor" title=" colour descriptor"> colour descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=opponent%20colour%20channel" title=" opponent colour channel"> opponent colour channel</a> </p> <a href="https://publications.waset.org/abstracts/31715/a-biologically-inspired-approach-to-automatic-classification-of-textile-fabric-prints-based-on-both-texture-and-colour-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31715.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">484</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3770</span> Gesture-Controlled Interface Using Computer Vision and Python</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vedant%20Vardhan%20Rathour">Vedant Vardhan Rathour</a>, <a href="https://publications.waset.org/abstracts/search?q=Anant%20Agrawal"> Anant Agrawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computer using hand gestures and voice commands. The system leverages advanced computer vision techniques using the MediaPipe framework and OpenCV to detect and interpret real time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the Speech Recognition library allows for seamless execution of tasks like web searches, location navigation and gesture control on the system through voice commands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title="gesture recognition">gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20tracking" title=" hand tracking"> hand tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/193844/gesture-controlled-interface-using-computer-vision-and-python" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193844.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">12</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3769</span> Non-Targeted Adversarial Object Detection Attack: Fast Gradient Sign Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandar%20Alahmadi">Bandar Alahmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Manohar%20Mareboyana"> Manohar Mareboyana</a>, <a href="https://publications.waset.org/abstracts/search?q=Lethia%20Jackson"> Lethia Jackson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, there are many applications that are using computer vision models, such as face recognition, image classification, and object detection. The accuracy of these models is very important for the performance of these applications. One challenge that facing the computer vision models is the adversarial examples attack. In computer vision, the adversarial example is an image that is intentionally designed to cause the machine learning model to misclassify it. One of very well-known method that is used to attack the Convolution Neural Network (CNN) is Fast Gradient Sign Method (FGSM). The goal of this method is to find the perturbation that can fool the CNN using the gradient of the cost function of CNN. In this paper, we introduce a novel model that can attack Regional-Convolution Neural Network (R-CNN) that use FGSM. We first extract the regions that are detected by R-CNN, and then we resize these regions into the size of regular images. Then, we find the best perturbation of the regions that can fool CNN using FGSM. Next, we add the resulted perturbation to the attacked region to get a new region image that looks similar to the original image to human eyes. Finally, we placed the regions back to the original image and test the R-CNN with the attacked images. Our model could drop the accuracy of the R-CNN when we tested with Pascal VOC 2012 dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adversarial%20examples" title="adversarial examples">adversarial examples</a>, <a href="https://publications.waset.org/abstracts/search?q=attack" title=" attack"> attack</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/103308/non-targeted-adversarial-object-detection-attack-fast-gradient-sign-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103308.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">193</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3768</span> Challenges in Video Based Object Detection in Maritime Scenario Using Computer Vision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dilip%20K.%20Prasad">Dilip K. Prasad</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Krishna%20Prasath"> C. Krishna Prasath</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepu%20Rajan"> Deepu Rajan</a>, <a href="https://publications.waset.org/abstracts/search?q=Lily%20Rachmawati"> Lily Rachmawati</a>, <a href="https://publications.waset.org/abstracts/search?q=Eshan%20Rajabally"> Eshan Rajabally</a>, <a href="https://publications.waset.org/abstracts/search?q=Chai%20Quek"> Chai Quek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20maritime%20vehicle" title="autonomous maritime vehicle">autonomous maritime vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=situation%20awareness" title=" situation awareness"> situation awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a> </p> <a href="https://publications.waset.org/abstracts/54887/challenges-in-video-based-object-detection-in-maritime-scenario-using-computer-vision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54887.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">457</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3767</span> Shared Vision System Support for Maintenance Tasks of Wind Turbines</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Buket%20Celik%20%C3%9Cnal">Buket Celik Ünal</a>, <a href="https://publications.waset.org/abstracts/search?q=Onur%20%C3%9Cnal"> Onur Ünal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Communication is the most challenging part of maintenance operations. Communication between expert and fieldworker is crucial for effective maintenance and this also affects the safety of the fieldworkers. To support a machine user in a remote collaborative physical task, both, a mobile and a stationary device are needed. Such a system is called a shared vision system and the system supports two people to solve a problem from different places. This system reduces the errors and provides a reliable support for qualified and less qualified users. Through this research, it was aimed to validate the effectiveness of using a shared vision system to facilitate communication between on-site workers and those issuing instructions regarding maintenance or inspection works over long distances. The system is designed with head-worn display which is called a shared vision system. As a part of this study, a substitute system is used and implemented by using a shared vision system for maintenance operation. The benefits of the use of a shared vision system are analyzed and results are adapted to the wind turbines to improve the occupational safety and health for maintenance technicians. The motivation for the research effort in this study can be summarized in the following research questions: -How can expert support technician over long distances during maintenance operation? -What are the advantages of using a shared vision system? Experience from the experiment shows that using a shared vision system is an advantage for both electrical and mechanical system failures. Results support that the shared vision system can be used for wind turbine maintenance and repair tasks. Because wind turbine generator/gearbox and the substitute system have similar failures. Electrical failures, such as voltage irregularities, wiring failures and mechanical failures, such as alignment, vibration, over-speed conditions are the common and similar failures for both. Furthermore, it was analyzed the effectiveness of the shared vision system by using a smart glasses in connection with the maintenance task performed by a substitute system under four different circumstances, namely by using a shared vision system, an audio communication, a smartphone and by yourself condition. A suitable method for determining dependencies between factors measured in Chi Square Test, and Chi Square Test for Independence measured for determining a relationship between two qualitative variables and finally Mann Whitney U Test is used to compare any two data sets. While based on this experiment, no relation was found between the results and the gender. Participants` responses confirmed that the shared vision system is efficient and helpful for maintenance operations. From the results of the research, there was a statistically significant difference in the average time taken by subjects on works using a shared vision system under the other conditions. Additionally, this study confirmed that a shared vision system provides reduction in time to diagnose and resolve maintenance issues, reduction in diagnosis errors, reduced travel costs for experts, and increased reliability in service. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication%20support" title="communication support">communication support</a>, <a href="https://publications.waset.org/abstracts/search?q=maintenance%20and%20inspection%20tasks" title=" maintenance and inspection tasks"> maintenance and inspection tasks</a>, <a href="https://publications.waset.org/abstracts/search?q=occupational%20health%20and%20safety" title=" occupational health and safety"> occupational health and safety</a>, <a href="https://publications.waset.org/abstracts/search?q=shared%20vision%20system" title=" shared vision system"> shared vision system</a> </p> <a href="https://publications.waset.org/abstracts/48026/shared-vision-system-support-for-maintenance-tasks-of-wind-turbines" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48026.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=machine%20vision&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=machine%20vision&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=machine%20vision&page=4">4</a></li> <li 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