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Object detection is a growing field of research in the field of computer vision. The ability to identify and classify objects, either in a single scene or in more than one frame, has gained huge importance in a variety of ways, as while operating a vehicle, the operator could even lack attention that could lead to disastrous collisions. In attempt to improve these perceivable problems, the Autonomous Vehicles and ADAS (Advanced Driver Assistance System) have considered to handle the task of identifying and classifying objects, which in turn use deep learning techniques such as the Faster Regional Convoluted Neural Network (F-RCNN), the You Only Look Once Model (YOLO), the Single Shot Detector (SSD) etc. to improve the precision of object detection. YOLO is a powerful technique as it achieves high precision whilst being able to manage in real time. This paper explains the architecture and working of YOLO algorithm for the purpose of detecting and classifying objects, trained on the classes from COCO dataset.","author":[{"@context":"https://schema.org","@type":"Person","name":"IJERT Journal","url":"https://independent.academia.edu/IJERTORG"}],"contributor":[],"dateCreated":"2021-04-01","datePublished":"2021-01-01","headline":"IJERT-Object Detection and Classification using YOLOv3","image":"https://attachments.academia-assets.com/66162765/thumbnails/1.jpg","inLanguage":"en","keywords":["Engineering","Engineering and Computer Science","Computer Science And Engineering","Electronic Engineering and Computer Science","Computer Science and Engineering"," Computer Science and Engg."],"publication":"International Journal of Engineering Research and Technology 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{"work":{"id":45661590,"created_at":"2021-04-01T23:29:54.301-07:00","from_world_paper_id":null,"updated_at":"2021-11-29T08:57:17.037-08:00","_data":{"abstract":"https://www.ijert.org/object-detection-and-classification-using-yolov3\n\nhttps://www.ijert.org/research/object-detection-and-classification-using-yolov3-IJERTV10IS020078.pdf\n\nAutonomous driving will increasingly require more and more dependable network-based mechanisms, requiring redundant, real-time implementations. Object detection is a growing field of research in the field of computer vision. The ability to identify and classify objects, either in a single scene or in more than one frame, has gained huge importance in a variety of ways, as while operating a vehicle, the operator could even lack attention that could lead to disastrous collisions. In attempt to improve these perceivable problems, the Autonomous Vehicles and ADAS (Advanced Driver Assistance System) have considered to handle the task of identifying and classifying objects, which in turn use deep learning techniques such as the Faster Regional Convoluted Neural Network (F-RCNN), the You Only Look Once Model (YOLO), the Single Shot Detector (SSD) etc. to improve the precision of object detection. YOLO is a powerful technique as it achieves high precision whilst being able to manage in real time. This paper explains the architecture and working of YOLO algorithm for the purpose of detecting and classifying objects, trained on the classes from COCO dataset.","publication_date":"2021,,","publication_name":"International Journal of Engineering Research and Technology (IJERT)"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"IJERT-Object Detection and Classification using YOLOv3","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [109571315]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":66162765,"attachmentType":"pdf"}"><img alt="First page of “IJERT-Object Detection and Classification using YOLOv3”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/66162765/mini_magick20210402-25314-1y9ifbm.png?1617348690" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">IJERT-Object Detection and Classification using YOLOv3</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="109571315" href="https://independent.academia.edu/IJERTORG"><img alt="Profile image of IJERT Journal" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/109571315/46811946/36059547/s65_ijert.journal.png" />IJERT Journal</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2021, International Journal of Engineering Research and Technology (IJERT)</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">6 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 45661590; 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Object detection is a growing field of research in the field of computer vision. The ability to identify and classify objects, either in a single scene or in more than one frame, has gained huge importance in a variety of ways, as while operating a vehicle, the operator could even lack attention that could lead to disastrous collisions. In attempt to improve these perceivable problems, the Autonomous Vehicles and ADAS (Advanced Driver Assistance System) have considered to handle the task of identifying and classifying objects, which in turn use deep learning techniques such as the Faster Regional Convoluted Neural Network (F-RCNN), the You Only Look Once Model (YOLO), the Single Shot Detector (SSD) etc. to improve the precision of object detection. YOLO is a powerful technique as it achieves high precision whilst being able to manage in real time. This paper explains the architecture and working of YOLO algorithm for the purpose of detecting and classifying objects, trained on the classes from COCO dataset.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":66162765,"attachmentType":"pdf","workUrl":"https://www.academia.edu/45661590/IJERT_Object_Detection_and_Classification_using_YOLOv3"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":66162765,"attachmentType":"pdf","workUrl":"https://www.academia.edu/45661590/IJERT_Object_Detection_and_Classification_using_YOLOv3"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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It is challenging to detect and classify objects. Recent advances in deep learning have allowed it to detect objects more accurately. In the past, there were several methods or tools used: R-CNN, Fast-RCNN, Faster-RCNN, YOLO, SSD, etc. This research focuses on "You Only Look Once" (YOLO) as a type of Convolutional Neural Network. Results will be accurate and timely when tested. So, we analysed YOLOv3's work by using Yolo3-tiny to detect both image and video objects.</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 Yolo","attachmentId":78453430,"attachmentType":"pdf","work_url":"https://www.academia.edu/67736659/Real_Time_Object_Detection_Using_Yolo","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/67736659/Real_Time_Object_Detection_Using_Yolo"><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="74581184" 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/74581184/Real_Time_Object_Detection_Using_YOLOv3">Real Time Object Detection Using YOLOv3</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="219977968" href="https://independent.academia.edu/shubhampatil629">shubham patil</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="132373791" href="https://independent.academia.edu/omasurekar">omkar masurekar</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">1,2,3,4 Student, Department of Computer Engineering, TEC, University of Mumbai, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Object detection using deep learning has achieved very good performance but there are many problems with images in real-world shooting such as noise, blurring or rotating jitter, etc. These problems have a great impact on object detection. The main objective is to detect objects using You Only Look Once (YOLO) approach. The YOLO method has several advantages as compared to other object detection algorithms. In other algorithms like Convolutional Neural Network (CNN), Fast-Convolutional Neural Network the algorithm will not look at the image completely, but in YOLO ,the algorithm looks the image completely by predicting the bounding boxes using convolutional network and finds class probabilities for these boxes and also detects the image faster ...</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 YOLOv3","attachmentId":82683426,"attachmentType":"pdf","work_url":"https://www.academia.edu/74581184/Real_Time_Object_Detection_Using_YOLOv3","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/74581184/Real_Time_Object_Detection_Using_YOLOv3"><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="122355932" 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/122355932/Enhancing_Object_Detection_Accuracy_Through_Custom_Dataset_Using_Yolo">Enhancing Object Detection Accuracy Through Custom Dataset Using Yolo</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 & Engineering Technology (IJRASET), 2024</p><p class="ds-related-work--abstract ds2-5-body-sm">Potholes pose significant risks to road safety and vehicle maintenance, leading to accidents and costly repairs. Traditional methods of pothole detection are often labour-intensive and time-consuming. In this study, we propose an innovative approach to pothole detection using YOLOv8, a state-of-the-art object detection algorithm. By harnessing the power of deep learning, our system can accurately identify and locate potholes in real-time video streams from traffic cameras and vehicles. We employ YOLOv8, an advanced variant of the You Only Look Once (YOLO) algorithm, known for its speed and accuracy in real-time object detection tasks. Leveraging a large annotated dataset of road images, we fine-tune the YOLOv8 model to specifically detect potholes. Our trained model is capable of identifying various pothole sizes and shapes, even in challenging lighting and weather conditions. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4,YOLOv4-tiny, and YOLOv5s) are experimented to measure their performance involved in real-time responsiveness and detection accuracy using the image set. The image set is identified by running the deep convolutional neural network (CNN) on several deep learning pothole detectors. After collecting a set of 600 images in 720×720 pixels resolution that captures various types of potholes on different road surface conditions, the set is divided into training, testing, and validation subset [1]. I. PROBLEM STATEMENT To implement real-time object detection and recognition in an images captured by webcam and videos in dynamic environment using deep learning model and YOLO ." The primary goal is to detect and recognition Objects in Real-time. We require rich data, all things considered. We need to observe the different type of objects which are moving in respect to the camera. It will help us with perceiving and in recognizing different objects collaboration and interaction. II.</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":"Enhancing Object Detection Accuracy Through Custom Dataset Using Yolo","attachmentId":117037844,"attachmentType":"pdf","work_url":"https://www.academia.edu/122355932/Enhancing_Object_Detection_Accuracy_Through_Custom_Dataset_Using_Yolo","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/122355932/Enhancing_Object_Detection_Accuracy_Through_Custom_Dataset_Using_Yolo"><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="68392059" 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/68392059/Design_and_Development_of_an_Autonomous_Car_using_Object_Detection_with_YOLOv4">Design and Development of an Autonomous Car using Object Detection with YOLOv4</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="144202495" href="https://tcetmumbai.academia.edu/AnujGoenka">Anuj Goenka</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2328357" href="https://independent.academia.edu/JournalofComputerScienceIJCSIS">Journal of Computer Science IJCSIS</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computer Science and Information Security (IJCSIS), Vol. 19, No. 12, December 2021, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Future cars are anticipated to be driverless; point-to-point transportation services capable of avoiding fatalities. To achieve this goal, auto-manufacturers have been investing to realize the potential autonomous driving. In this regard, we present a self-driving model car capable of autonomous driving using object-detection as a primary means of steering, on a track made of colored cones. This paper goes through the process of fabricating a model vehicle, from its embedded hardware platform, to the end-to-end ML pipeline necessary for automated data acquisition and model-training, thereby allowing a Deep Learning model to derive input from the hardware platform to control the car's movements. This guides the car autonomously and adapts well to real-time tracks without manual feature-extraction. This paper presents a Computer Vision model that learns from video data and involves Image Processing, Augmentation, Behavioral Cloning and a Convolutional Neural Network model. The Darknet architecture is used to detect objects through a video segment and convert it into a 3D navigable path. Finally, the paper touches upon the conclusion, results and scope of future improvement in the technique used.</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":"Design and Development of an Autonomous Car using Object Detection with YOLOv4","attachmentId":78882511,"attachmentType":"pdf","work_url":"https://www.academia.edu/68392059/Design_and_Development_of_an_Autonomous_Car_using_Object_Detection_with_YOLOv4","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/68392059/Design_and_Development_of_an_Autonomous_Car_using_Object_Detection_with_YOLOv4"><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="48839724" 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/48839724/Real_Time_Object_Detection_using_YOLO_A_review">Real-Time Object Detection using YOLO: A review</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="193375969" href="https://independent.academia.edu/LakshiniKuganandamurthy">Lakshini Kuganandamurthy</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="147848209" href="https://iuni-saarland.academia.edu/UpulieHandalage">Upulie Handalage</a></div><p class="ds-related-work--abstract ds2-5-body-sm">With the availability of enormous amounts of data and the need to computerize visual-based systems, research on object detection has been the focus for the past decade. This need has been accelerated with the increasing computational power and Convolutional Neural Network (CNN) advancements since 2012. With various CNN network architectures available, the You Only Look Once (YOLO) network is popular due to its many reasons, mainly its speed of identification applicable in real-time object identification. Followed by a general introduction of the background and CNN, this paper wishes to review the innovative, yet comparatively simple approach YOLO takes at object detection.</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 YOLO: A review","attachmentId":67257544,"attachmentType":"pdf","work_url":"https://www.academia.edu/48839724/Real_Time_Object_Detection_using_YOLO_A_review","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/48839724/Real_Time_Object_Detection_using_YOLO_A_review"><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="87628836" 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/87628836/Evaluation_of_Deep_Learning_YOLOv3_Algorithm_for_Object_Detection_and_Classification">Evaluation of Deep Learning YOLOv3 Algorithm for Object Detection and Classification</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="45815667" href="https://independent.academia.edu/NabilIsmail3">Nabil Ismail</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Menoufia Journal of Electronic Engineering Research</p><p class="ds-related-work--abstract ds2-5-body-sm">You Only Look Once version 3 (YOLOv3) is a deep learning model for object detection and classification. It is a single neural network architecture model that uses features from the feeding images and predicts bounding box for all classes of image simultaneously. This paper descript an experimental work for train the deep learning model based on YOLOv3 architecture implemented using Tensor Flow as a deep learning framework. The training process had been done using the data-set PASCAL VOC 2007 and data-set PASCAL VOC 2012 and using The Adaptive Moment Estimation Optimizer (ADM optimizer). The trained model is then tested by using the VOC 2007 test data-set. The final results evaluate the YOLOv3 deep learning model performance for object detection and classification.</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":"Evaluation of Deep Learning YOLOv3 Algorithm for Object Detection and Classification","attachmentId":91786325,"attachmentType":"pdf","work_url":"https://www.academia.edu/87628836/Evaluation_of_Deep_Learning_YOLOv3_Algorithm_for_Object_Detection_and_Classification","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/87628836/Evaluation_of_Deep_Learning_YOLOv3_Algorithm_for_Object_Detection_and_Classification"><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="90220055" 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/90220055/Object_Detection_in_Autonomous_Vehicles">Object Detection in Autonomous Vehicles</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="36969468" href="https://pub.academia.edu/MariusVochin">Marius C Vochin</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2022 25th International Symposium on Wireless Personal Multimedia Communications (WPMC), 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">In the coming years, autonomous driving will be the primary focus of the automobile industry. The great majority of accidents are caused by human mistakes, and autonomous cars can help to lower this number significantly, thus improving road safety. Object identification plays a critical part in autonomous vehicle driving, and deep learning techniques are used to implement it. YOLO is one of the most common methods for recognizing and identifying things that emerge on the road. Its popularity has developed as a result of its superior performance in terms of speed, high accuracy, and learning capabilities when compared to other object recognition approaches such as Retina-Net, fast R-CNN, and Single-Shot MultiBox Detection (SSD).</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 in Autonomous Vehicles","attachmentId":93842485,"attachmentType":"pdf","work_url":"https://www.academia.edu/90220055/Object_Detection_in_Autonomous_Vehicles","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/90220055/Object_Detection_in_Autonomous_Vehicles"><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="110953191" 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/110953191/Yolo_ros_Convolutional_Neural_Networks_object_detection">Yolo/ros/Convolutional Neural Networks/object 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="206996810" href="https://independent.academia.edu/AyaMahmood3">Aya Mahmood</a></div><p class="ds-related-work--metadata ds2-5-body-xs">yolo, 2023</p><p class="ds-related-work--abstract ds2-5-body-sm">Introduction • Application • When it comes to deep learning-based object detection, there are three primary object detectors you'll encounter • Object Detection Metrics and Non-Maximum Suppression (NMS) • How AP works? • Non-Maximum Suppression (NMS) 2-Chapter II (Convolutional Neural Networks)</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":"Yolo/ros/Convolutional Neural Networks/object detection","attachmentId":108610215,"attachmentType":"pdf","work_url":"https://www.academia.edu/110953191/Yolo_ros_Convolutional_Neural_Networks_object_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/110953191/Yolo_ros_Convolutional_Neural_Networks_object_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="8" data-entity-id="50954646" 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/50954646/IRJET_OBJECT_DETECTION_AND_CLASSIFICATION_USING_YOLOV3">IRJET- OBJECT DETECTION AND CLASSIFICATION USING YOLOV3</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, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Object detection has several advantages in computer vision technologies. It is used in image retrieval, security, observations, etc. The goal of object detection system is object localization and identifying the category to which the object belongs. In this paper, a deep learning algorithm YOLO (You Only Look Once) is used for object detection and classification. This proposed method yields mean average precision (mAP) of 95% for traffic scenario images in identifying traffic lights, car, bus, person and motorcycle.</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":"IRJET- OBJECT DETECTION AND CLASSIFICATION USING YOLOV3","attachmentId":68832119,"attachmentType":"pdf","work_url":"https://www.academia.edu/50954646/IRJET_OBJECT_DETECTION_AND_CLASSIFICATION_USING_YOLOV3","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/50954646/IRJET_OBJECT_DETECTION_AND_CLASSIFICATION_USING_YOLOV3"><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="115187077" 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/115187077/Comparative_Study_of_Some_Deep_Learning_Object_Detection_Algorithms_R_CNN_FAST_R_CNN_FASTER_R_CNN_SSD_and_YOLO">Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO</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="182343151" href="https://afit.academia.edu/OluwaseyiOlorunshola">Dr. Oluwaseyi Olorunshola</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Due to its numerous applications and new technological advancements, object detection has gained more attention in the last few years. This study examined various uses of some deep learning object detection algorithms. These algorithms are divided into two-stage detectors like Region Based Convolutional Neural Network (R-CNN), Fast Region Based Convolutional Neural Network (Faster R-CNN), and Faster Region Based Convolutional Neural Network (Faster R-CNN), and one-stage detectors like Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) algorithms that are used in text and face detection, image retrieval, security, surveillance, traffic control, traffic sign/light detection, pedestrian detection and in medical areas among others. This research primarily focuses on three applications: drone surveillance, applications relating to traffic, and medical fields. Findings from the performed analysis indicate that YOLO stands out as the predominant algorithm for drone surveillance among different deep learning models used in various application fields and being a one-stage detector. In terms of usage in traffic-related applications, SSD proved to be a prominent one-stage detector alongside Faster R-CNN which gained popularity as a two-stage detector preferred for applications in the medical field.</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":"Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO","attachmentId":111668181,"attachmentType":"pdf","work_url":"https://www.academia.edu/115187077/Comparative_Study_of_Some_Deep_Learning_Object_Detection_Algorithms_R_CNN_FAST_R_CNN_FASTER_R_CNN_SSD_and_YOLO","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/115187077/Comparative_Study_of_Some_Deep_Learning_Object_Detection_Algorithms_R_CNN_FAST_R_CNN_FASTER_R_CNN_SSD_and_YOLO"><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":66162765,"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":66162765,"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_66162765" 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. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="67350326" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/67350326/IRJET_Design_and_Development_of_an_Autonomous_Car_using_Object_Detection_with_YOLOv4">IRJET- Design and Development of an Autonomous Car using Object Detection with YOLOv4</a><div class="ds-related-work--metadata"><a class="js-related-work-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, 2021</p><div 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