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(PDF) Parallel Convolutional Neural Networks for Object Detection | Yanxia Sun - Academia.edu
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style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/88375158/A_Review_of_Object_Detection_Models_Based_on_Convolutional_Neural_Network"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="45118343" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/45118343/Deep_Learning_Based_on_Parallel_CNNs_for_Pedestrian_Detection">Deep Learning Based on Parallel CNNs for Pedestrian Detection</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="185376224" href="https://itrc.academia.edu/IJICTR">IJICTR Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Information and Communication Technology Research, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">Recently, deep learning methods, mostly algorithms based on Deep Convolutional Neural Networks (DCNNs) have yielded great results on pedestrian detection. Algorithms based on DCNNs spontaneously learn features in a supervised manner and are able to learn qualified high level feature representations to detect pedestrian. In this paper, we first review a number of popular DCNN-based training approaches along with their recent extensions. We then briefly describe recent algorithms based on these approaches. Also, we accentuate recent contributions and main challenges of DCNNs in detecting pedestrian. We analyze deep pedestrian detection algorithms from training approach, categorization, and DCNN model points of view, and ultimately propose a new deep architecture and training approach for deep pedestrian detection. The experimental results show that the proposed DCNN and training approach, achieve more accurate rate detection than the previously reported architectures and training approaches.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Deep Learning Based on Parallel CNNs for Pedestrian Detection","attachmentId":65685438,"attachmentType":"pdf","work_url":"https://www.academia.edu/45118343/Deep_Learning_Based_on_Parallel_CNNs_for_Pedestrian_Detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/45118343/Deep_Learning_Based_on_Parallel_CNNs_for_Pedestrian_Detection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="44667910" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/44667910/Convolutional_Neural_Network_CNN_for_Image_Detection_and_Recognition">Convolutional Neural Network (CNN) for Image Detection and Recognition</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="180978298" href="https://espol.academia.edu/Ra%C3%BAlAlbertoMira">Raúl Alberto Mira</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Deep Learning algorithms are designed in such a way that they mimic the function of the human cerebral cortex. These algorithms are representations of deep neural networks i.e. neural networks with many hidden layers. Convolutional neural networks are deep learning algorithms that can train large datasets with millions of parameters, in form of 2D images as input and convolve it with filters to produce the desired outputs. In this article, CNN models are built to evaluate its performance on image recognition and detection datasets. The algorithm is implemented on MNIST and CIFAR-10 dataset and its performance are evaluated. The accuracy of models on MNIST is 99.6 %, CIFAR-10 is using real-time data augmentation and dropout on CPU unit.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Convolutional Neural Network (CNN) for Image Detection and Recognition","attachmentId":65140855,"attachmentType":"pdf","work_url":"https://www.academia.edu/44667910/Convolutional_Neural_Network_CNN_for_Image_Detection_and_Recognition","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/44667910/Convolutional_Neural_Network_CNN_for_Image_Detection_and_Recognition"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="77160340" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/77160340/REVIEW_ON_OBJECT_DETECTION_WITH_CNN">REVIEW ON OBJECT DETECTION WITH CNN</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31493941" href="https://irjet.academia.edu/IRJET">IRJET Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IRJET, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Object detection is one of the most important achievements of deep learning and image processing since it finds and recognises objects in images. An object detection model may be trained to recognise and detect several objects, making it adaptable. Object detection had a large scope of interest even before deep learning approaches and modern-day image processing capabilities. It has become considerably more widespread in the present generation, thanks to the development of convolutional neural networks (CNNs) and the adaptation of computer vision technology. The latest wave of deep learning approaches to object detection gives up apparently limitless possibilities.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"REVIEW ON OBJECT DETECTION WITH CNN","attachmentId":84604028,"attachmentType":"pdf","work_url":"https://www.academia.edu/77160340/REVIEW_ON_OBJECT_DETECTION_WITH_CNN","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/77160340/REVIEW_ON_OBJECT_DETECTION_WITH_CNN"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="89238819" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89238819/Real_Time_Object_Detection_using_CNN">Real Time Object Detection using CNN</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="242195637" href="https://independent.academia.edu/AkashTripathi96">Akash Tripathi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Engineering &amp; Technology, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">Achieving new heights in object detection and image classification was made possible because of Convolution Neural Network(CNN). However, compared to image classification the object detection tasks are more difficult to analyze, more energy consuming and computation intensive. To overcome these challenges, a novel approach is developed for real time object detection applications to improve the accuracy and energy efficiency of the detection process. This is achieved by integrating the Convolutional Neural Networks (CNN) with the Scale Invariant Feature Transform (SIFT) algorithm. Here, we obtain high accuracy output with small sample data to train the model by integrating the CNN and SIFT features. The proposed detection model is a cluster of multiple deep convolutional neural networks and hybrid CNN-SIFT algorithm. The reason to use the SIFT featureis to amplify the model‟s capacity to detect small data or features as the SIFT requires small datasets to detect objects. Our simulati...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Real Time Object Detection using CNN","attachmentId":93074247,"attachmentType":"pdf","work_url":"https://www.academia.edu/89238819/Real_Time_Object_Detection_using_CNN","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/89238819/Real_Time_Object_Detection_using_CNN"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="41245466" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/41245466/Computation_Image_Classification_and_Object_Detection_Algorithm_Based_on_Convolutional_Neural_Network">Computation Image Classification and Object Detection Algorithm Based on Convolutional Neural Network</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="107601574" href="https://independent.academia.edu/InsightsPublisher">Insights Publisher</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Science Insights, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Traditional image classification methods are difficult to process huge image data and cannot meet people's requirements for image classification accuracy and speed. Convolutional neural networks have achieved a series of breakthrough research results in image classification, object detection, and image semantic segmentation. This method broke through the bottleneck of traditional image classification methods and became the mainstream algorithm for image classification. Its powerful feature learning and classification capabilities have attracted widespread attention. How to effectively use convolutional neural networks to classify images have become research hotspots. In this paper, after a systematic study of convolutional neural networks and an in-depth study of the application of convolutional neural networks in image processing, the mainstream structural models, advantages and disadvantages, time / space used in image classification based on convolutional neural networks are given. Complexity, problems that may be encountered during model training, and corresponding solutions. At the same time, the generative adversarial network and capsule network based on the deep learning-based image classification extension model are also introduced; simulation experiments verify the image classification In terms of accuracy, the image classification method based on convolutional neural networks is superior to traditional image classification methods. At the same time, the performance differences between the currently popular convolutional neural network models are comprehensively compared and the advantages and disadvantages of various models are further verified. Experiments and analysis of overfitting problem, data set construction method, generative adversarial network and capsule network performance.■</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Computation Image Classification and Object Detection Algorithm Based on Convolutional Neural Network","attachmentId":61469194,"attachmentType":"pdf","work_url":"https://www.academia.edu/41245466/Computation_Image_Classification_and_Object_Detection_Algorithm_Based_on_Convolutional_Neural_Network","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/41245466/Computation_Image_Classification_and_Object_Detection_Algorithm_Based_on_Convolutional_Neural_Network"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="43726642" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/43726642/Object_Detection_using_Deep_Learning_Algorithm_CNN">Object Detection using Deep Learning Algorithm CNN</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="6079060" href="https://independent.academia.edu/IJRASETPublication">IJRASET Publication</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Object Detection using Deep Learning Algorithm CNN","attachmentId":64035558,"attachmentType":"pdf","work_url":"https://www.academia.edu/43726642/Object_Detection_using_Deep_Learning_Algorithm_CNN","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/43726642/Object_Detection_using_Deep_Learning_Algorithm_CNN"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="79688710" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/79688710/Deep_Learning_Object_Detector_Using_a_Combination_of_Convolutional_Neural_Network_CNN_Architecture_MiniVGGNet_and_Classic_Object_Detection_Algorithm">Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="10246969" href="https://independent.academia.edu/azurachesoh">azura che soh</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Pertanika Journal of Science and Technology, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding wind...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm","attachmentId":86317614,"attachmentType":"pdf","work_url":"https://www.academia.edu/79688710/Deep_Learning_Object_Detector_Using_a_Combination_of_Convolutional_Neural_Network_CNN_Architecture_MiniVGGNet_and_Classic_Object_Detection_Algorithm","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/79688710/Deep_Learning_Object_Detector_Using_a_Combination_of_Convolutional_Neural_Network_CNN_Architecture_MiniVGGNet_and_Classic_Object_Detection_Algorithm"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="39182581" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/39182581/Classification_of_Image_using_Convolutional_Neural_Network_CNN">Classification of Image using Convolutional Neural Network (CNN</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="114850190" href="https://independent.academia.edu/MdShahriarAlamSajib">Md. Shahriar Alam Sajib</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3241530" href="https://pabna.academia.edu/MdAnwarHossain">Md. Anwar Hossain</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Global Journal of Computer Science and Technology: D Neural & Artificial Intelligence, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. We have used Convolutional Neural Networks (CNN) in automatic image classification systems. In most cases, we utilize the features from the top layer of the CNN for classification; however, those features may not contain enough useful information to predict an image correctly. In some cases, features from the lower layer carry more discriminative power than those from the top. Therefore, applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN's potential discriminant power to its full extent. Because of this property we are in need of fusion of features from multiple layers. We want to create a model with multiple layers that will be able to recognize and classify the images. We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset. Moreover, we will show how MatConvNet can be used to implement our model with CPU training as well as less training time. The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Classification of Image using Convolutional Neural Network (CNN","attachmentId":59309469,"attachmentType":"pdf","work_url":"https://www.academia.edu/39182581/Classification_of_Image_using_Convolutional_Neural_Network_CNN","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/39182581/Classification_of_Image_using_Convolutional_Neural_Network_CNN"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="108017557" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/108017557/Object_Detection_System_Based_on_Convolution_Neural_Networks_Using_Single_Shot_Multi_Box_Detector">Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="177443884" href="https://independent.academia.edu/SonamSrivastava12">Sonam Srivastava</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Procedia Computer Science, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Object Detection System Based on Convolution Neural Networks Using Single Shot Multi-Box Detector","attachmentId":106516328,"attachmentType":"pdf","work_url":"https://www.academia.edu/108017557/Object_Detection_System_Based_on_Convolution_Neural_Networks_Using_Single_Shot_Multi_Box_Detector","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/108017557/Object_Detection_System_Based_on_Convolution_Neural_Networks_Using_Single_Shot_Multi_Box_Detector"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":95562308,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":95562308,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_95562308" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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