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Now a day&#39;s automotive companies, technology companies, government bodies, research" /> <title>(PDF) An efficient object detection by autonomous vehicle using deep learning | International Journal of Electrical and Computer Engineering (IJECE) - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/120891378/An_efficient_object_detection_by_autonomous_vehicle_using_deep_learning" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '49879c2402910372f4abc62630a427bbe033d190'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1732472383000); window.Aedu.timeDifference = new Date().getTime() - 1732472383000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day\u0026amp;#39;s automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are mainly on-road vehicles, traffic signals, cars, trucks and buses. These inputs are given to the models to predict and detect the objects. The Performance of the proposed system is compared with performance of deep learning algorithms convolution neural network (CNN). The proposed system accuracy greater than 76.5% to 93.3%, mean average precision (Map) and frame per second (FPS) are 0.895 and 43.95%.","author":[{"@context":"https://schema.org","@type":"Person","name":"International Journal of Electrical and Computer Engineering (IJECE)"}],"contributor":[],"dateCreated":"2024-06-11","dateModified":"2024-06-11","datePublished":"2024-01-01","headline":"An efficient object detection by autonomous vehicle using deep learning","inLanguage":"en","keywords":["Computer Vision","Convolutional Neural Network"],"locationCreated":null,"publication":"International Journal of Electrical and Computer Engineering (IJECE)","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/120891378/An_efficient_object_detection_by_autonomous_vehicle_using_deep_learning","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}]}</script><link 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"https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F120891378%2FAn_efficient_object_detection_by_autonomous_vehicle_using_deep_learning%3Fauto%3Ddownload"; window.loswp.translateUrl = "https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F120891378%2FAn_efficient_object_detection_by_autonomous_vehicle_using_deep_learning%3Fshow_translation%3Dtrue"; window.loswp.previewableAttachments = [{"id":115901107,"identifier":"Attachment_115901107","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":120891378,"created_at":"2024-06-11T18:20:53.021-07:00","from_world_paper_id":null,"updated_at":"2024-06-20T05:43:15.974-07:00","_data":{"doi":"10.11591/ijece.v14i4.pp4287-4295","abstract":"The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day's automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are mainly on-road vehicles, traffic signals, cars, trucks and buses. These inputs are given to the models to predict and detect the objects. The Performance of the proposed system is compared with performance of deep learning algorithms convolution neural network (CNN). The proposed system accuracy greater than 76.5% to 93.3%, mean average precision (Map) and frame per second (FPS) are 0.895 and 43.95%.","publication_date":"2024,,","publication_name":"International Journal of Electrical and Computer Engineering (IJECE)"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"An efficient object detection by autonomous vehicle using deep learning","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [163474776]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</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":115901107,"attachmentType":"pdf"}"><img alt="First page of “An efficient object detection by autonomous vehicle using deep learning”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/115901107/mini_magick20240802-1-keiilr.png?1722563656" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.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">An efficient object detection by autonomous vehicle using deep learning</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="163474776" href="https://independent.academia.edu/JournalIJECE"><img alt="Profile image of International Journal of Electrical and Computer Engineering (IJECE)" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/163474776/123357473/112705609/s65_international_journal_of_electrical_and_computer_engineering._ijece_.jpg" />International Journal of Electrical and Computer Engineering (IJECE)</a></div><p class="ds-work-card--detail ds2-5-body-sm">2024, International Journal of Electrical and Computer Engineering (IJECE)</p><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day's automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are mainly on-road vehicles, traffic signals, cars, trucks and buses. These inputs are given to the models to predict and detect the objects. The Performance of the proposed system is compared with performance of deep learning algorithms convolution neural network (CNN). The proposed system accuracy greater than 76.5% to 93.3%, mean average precision (Map) and frame per second (FPS) are 0.895 and 43.95%.</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":115901107,"attachmentType":"pdf","workUrl":"https://www.academia.edu/120891378/An_efficient_object_detection_by_autonomous_vehicle_using_deep_learning"}">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":115901107,"attachmentType":"pdf","workUrl":"https://www.academia.edu/120891378/An_efficient_object_detection_by_autonomous_vehicle_using_deep_learning"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="115901107" data-landing_url="https://www.academia.edu/120891378/An_efficient_object_detection_by_autonomous_vehicle_using_deep_learning" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="77593574" 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/77593574/An_Improved_Deep_Learning_Solution_for_Object_Detection_in_Self_Driving_Cars">An Improved Deep Learning Solution for Object Detection in Self-Driving Cars</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="72957250" href="https://independent.academia.edu/minamobahi">mina mobahi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Reliable object detection is one of the most important requirements of environment perception in autonomous driving. The goal of this research is to find a convenient solution to detect objects in images from the self-driving car medium. Convolutional neural networks (CNNs) are deep neural networks used in image processing, object classification, and object recognition. Therefore, deep convolution networks are employed in this project to identify objects accurately. In order to train and evaluate the neural network, we used BDD100K dataset which is one of the largest open-source datasets in autonomous driving published by Berkeley University. The approach used in the proposed algorithm is to apply the feature pyramid network along with a single-stage object detector, which enhances the accuracy of object detection. In addition, it improves the detection of different scales, especially small ones compared to those of the previous works, leading to increased safety and security in sel...</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":"An Improved Deep Learning Solution for Object Detection in Self-Driving Cars","attachmentId":84917215,"attachmentType":"pdf","work_url":"https://www.academia.edu/77593574/An_Improved_Deep_Learning_Solution_for_Object_Detection_in_Self_Driving_Cars","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/77593574/An_Improved_Deep_Learning_Solution_for_Object_Detection_in_Self_Driving_Cars"><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="103380713" 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/103380713/Vehicle_detection_systems_for_intelligent_driving_using_deep_convolutional_neural_networks">Vehicle detection systems for intelligent driving using deep convolutional neural networks</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="40045720" href="https://independent.academia.edu/Abiyev">Rahib Abiyev</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Discover Artificial Intelligence</p><p class="ds-related-work--abstract ds2-5-body-sm">In the paper, a vision-based vehicle identification system is proposed for autonomous intelligent car driving. The accurate detection of obstacles (vehicles) during intelligent car driving allows avoiding crashes, preventing accidents, saving people’s lives and reducing harm. The vehicle detection system, which uses low-quality images captured by a monocular video camera mounted at the front of the car, is based on convolutional neural networks (CNN). The CNN can extract global features of the images using convolutional layers and achieves more accurate, and faithful contours of vehicles. The CNN structure proposed in the paper provides high-accuracy detection of vehicle images. The experiments that have been performed using GTI dataset demonstrate that the CNN-based vehicle detection system achieves very accurate results and is more robust to different variations of images.</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":"Vehicle detection systems for intelligent driving using deep convolutional neural networks","attachmentId":103402835,"attachmentType":"pdf","work_url":"https://www.academia.edu/103380713/Vehicle_detection_systems_for_intelligent_driving_using_deep_convolutional_neural_networks","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/103380713/Vehicle_detection_systems_for_intelligent_driving_using_deep_convolutional_neural_networks"><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="94525155" 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/94525155/Road_Object_Detection_A_Comparative_Study_of_Deep_Learning_Based_Algorithms">Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms</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="252350507" href="https://independent.academia.edu/MalikHaris23">Malik Haris</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Electronics, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Automated driving and vehicle safety systems need object detection. It is important that object detection be accurate overall and robust to weather and environmental conditions and run in real-time. As a consequence of this approach, they require image processing algorithms to inspect the contents of images. This article compares the accuracy of five major image processing algorithms: Region-based Fully Convolutional Network (R-FCN), Mask Region-based Convolutional Neural Networks (Mask R-CNN), Single Shot Multi-Box Detector (SSD), RetinaNet, and You Only Look Once v4 (YOLOv4). In this comparative analysis, we used a large-scale Berkeley Deep Drive (BDD100K) dataset. Their strengths and limitations are analyzed based on parameters such as accuracy (with/without occlusion and truncation), computation time, precision-recall curve. The comparison is given in this article helpful in understanding the pros and cons of standard deep learning-based algorithms while operating under real-tim...</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":"Road Object Detection: A Comparative Study of Deep Learning-Based Algorithms","attachmentId":96956836,"attachmentType":"pdf","work_url":"https://www.academia.edu/94525155/Road_Object_Detection_A_Comparative_Study_of_Deep_Learning_Based_Algorithms","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/94525155/Road_Object_Detection_A_Comparative_Study_of_Deep_Learning_Based_Algorithms"><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="43669902" 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/43669902/OBJECT_DETECTION_IN_TRAFFIC_SCENARIOS_A_COMPARISON_OF_TRADITIONAL_AND_DEEP_LEARNING_APPROACHES">OBJECT DETECTION IN TRAFFIC SCENARIOS -A COMPARISON OF TRADITIONAL AND DEEP LEARNING APPROACHES</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="15689241" href="https://independent.academia.edu/ComputerScienceInformationTechnologyCSIT">Computer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP)</a></div><p class="ds-related-work--abstract ds2-5-body-sm">In the area of computer vision, research on object detection algorithms has grown rapidly as it is the fundamental step for automation, specifically for self-driving vehicles. This work presents a comparison of traditional and deep learning approaches for the task of object detection in traffic scenarios. The handcrafted feature descriptor like Histogram of oriented Gradients (HOG) with a linear Support Vector Machine (SVM) classifier is compared with deep learning approaches like Single Shot Detector (SSD) and You Only Look Once (YOLO), in terms of mean Average Precision (mAP) and processing speed. SSD algorithm is implemented with different backbone architectures like VGG16, MobileNetV2 and ResNeXt50, similarly YOLO algorithm with MobileNetV1 and ResNet50, to compare the performance of the approaches. The training and inference is performed on PASCAL VOC 2007 and 2012 training, and PASCAL VOC 2007 test data respectively. We consider five classes relevant for traffic scenarios, namely, bicycle, bus, car, motorbike and person for the calculation of mAP. Both qualitative and quantitative results are presented for comparison. For the task of object detection, the deep learning approaches outperform the traditional approach both in accuracy and speed. This is achieved at the cost of requiring large amount of data, high computation power and time to train a deep learning approach.</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 TRAFFIC SCENARIOS -A COMPARISON OF TRADITIONAL AND DEEP LEARNING APPROACHES","attachmentId":63971211,"attachmentType":"pdf","work_url":"https://www.academia.edu/43669902/OBJECT_DETECTION_IN_TRAFFIC_SCENARIOS_A_COMPARISON_OF_TRADITIONAL_AND_DEEP_LEARNING_APPROACHES","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/43669902/OBJECT_DETECTION_IN_TRAFFIC_SCENARIOS_A_COMPARISON_OF_TRADITIONAL_AND_DEEP_LEARNING_APPROACHES"><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="93292664" 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/93292664/A_Vehicle_Detection_Approach_using_Deep_Learning_Methodologies">A Vehicle Detection Approach using Deep Learning Methodologies</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="66264307" href="https://ankara.academia.edu/%C4%B0manAskerbeyli">İman Askerbeyli</a></div><p class="ds-related-work--metadata ds2-5-body-xs">ArXiv, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">The purpose of this study is to successfully train our vehicle detector using R-CNN, Faster R-CNN deep learning methods on a sample vehicle data sets and to optimize the success rate of the trained detector by providing efficient results for vehicle detection by testing the trained vehicle detector on the test data. The working method consists of six main stages. These are respectively; loading the data set, the design of the convolutional neural network, configuration of training options, training of the Faster R-CNN object detector and evaluation of trained detector. In addition, in the scope of the study, Faster R-CNN, R-CNN deep learning methods were mentioned and experimental analysis comparisons were made with the results obtained from vehicle 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":"A Vehicle Detection Approach using Deep Learning Methodologies","attachmentId":96070035,"attachmentType":"pdf","work_url":"https://www.academia.edu/93292664/A_Vehicle_Detection_Approach_using_Deep_Learning_Methodologies","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/93292664/A_Vehicle_Detection_Approach_using_Deep_Learning_Methodologies"><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="121436455" 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/121436455/A_Fast_and_Accurate_Real_Time_Vehicle_Detection_Method_Using_Deep_Learning_for_Unconstrained_Environments">A Fast and Accurate Real-Time Vehicle Detection Method Using Deep Learning for Unconstrained Environments</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="300174126" href="https://independent.academia.edu/UzairKhan696">Uzair Khan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Applied Sciences</p><p class="ds-related-work--abstract ds2-5-body-sm">Deep learning-based classification and detection algorithms have emerged as a powerful tool for vehicle detection in intelligent transportation systems. The limitations of the number of high-quality labeled training samples makes the single vehicle detection methods incapable of accomplishing acceptable accuracy in road vehicle detection. This paper presents detection and classification of vehicles on publicly available datasets by utilizing the YOLO-v5 architecture. This paper’s findings utilize the concept of transfer learning through fine tuning the weights of the pre-trained YOLO-v5 architecture. To employ the concept of transfer learning, extensive data sets of images and videos of the congested traffic patterns were collected by the authors. These datasets were made more comprehensive by pointing various attributes, for instance high- and low-density traffic patterns, occlusions, and different weather circumstances. All of these gathered datasets were manually annotated. Ultim...</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":"A Fast and Accurate Real-Time Vehicle Detection Method Using Deep Learning for Unconstrained Environments","attachmentId":116311986,"attachmentType":"pdf","work_url":"https://www.academia.edu/121436455/A_Fast_and_Accurate_Real_Time_Vehicle_Detection_Method_Using_Deep_Learning_for_Unconstrained_Environments","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/121436455/A_Fast_and_Accurate_Real_Time_Vehicle_Detection_Method_Using_Deep_Learning_for_Unconstrained_Environments"><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="112084286" 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/112084286/Vehicle_Detection_and_Identification_Using_Computer_Vision_Technology_with_the_Utilization_of_the_YOLOv8_Deep_Learning_Method">Vehicle Detection and Identification Using Computer Vision Technology with the Utilization of the YOLOv8 Deep Learning Method</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="218588178" href="https://independent.academia.edu/TELAUMBANUAPASRAH">PASRAH TELAUMBANUA</a></div><p class="ds-related-work--metadata ds2-5-body-xs">sinkron</p><p class="ds-related-work--abstract ds2-5-body-sm">Vehicle identification and detection is an important part of building intelligent transportation. Various methods have been proposed in this field, but recently the YOLOv8 model has been proven to be one of the most accurate methods applied in various fields. In this study, we propose a YOLOv8 model approach for the identification and detection of 9 vehicle classes in a reprocessed image data set. The steps are carried out by adding labels to the dataset which consists of 2,042 image data for training, 204 validation images and 612 test data. From the results of the training, it produces an accuracy value of 77% with the setting of epoch = 100, batch = 8 and image size of 640. For testing, the YOLOv8 model can detect the type of vehicle on video assets recorded by vehicle activity at intersections with. However, the occlusion problem overlapping vehicle objects has a significant impact on the accuracy value, so it needs to be improved. In addition, the addition of image datasets and...</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":"Vehicle Detection and Identification Using Computer Vision Technology with the Utilization of the YOLOv8 Deep Learning Method","attachmentId":109424272,"attachmentType":"pdf","work_url":"https://www.academia.edu/112084286/Vehicle_Detection_and_Identification_Using_Computer_Vision_Technology_with_the_Utilization_of_the_YOLOv8_Deep_Learning_Method","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/112084286/Vehicle_Detection_and_Identification_Using_Computer_Vision_Technology_with_the_Utilization_of_the_YOLOv8_Deep_Learning_Method"><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="97082644" 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/97082644/Vehicle_Detection_for_Vision_Based_Intelligent_Transportation_Systems_Using_Convolutional_Neural_Network_Algorithm">Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network 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="4676562" href="https://redseauniveristy.academia.edu/elmustafasayed">Elmustafa S A Y E D Ali</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="113737" href="https://iium.academia.edu/OTHMANOKHALIFA">OTHMAN O KHALIFA</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. erefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset.</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":"Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm","attachmentId":98803146,"attachmentType":"pdf","work_url":"https://www.academia.edu/97082644/Vehicle_Detection_for_Vision_Based_Intelligent_Transportation_Systems_Using_Convolutional_Neural_Network_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/97082644/Vehicle_Detection_for_Vision_Based_Intelligent_Transportation_Systems_Using_Convolutional_Neural_Network_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="72570607" 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/72570607/Image_Recognition_by_Using_a_Convolutional_Neural_Network_to_Identify_Objects_for_Driverless_Car">Image Recognition by Using a Convolutional Neural Network to Identify Objects for Driverless Car</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), 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">The concept of the paper was inspired by the recent surge in the automated car industry. The designed car was capable of detecting the road signals and taking the right and left turns accordingly. Object detection is a key ability required by most computer used in automated vehicles. The latest research in this area has been making great progress in many directions. Object detection and tracking has a variety of uses, our paper explain how to use convolutional neural network for object detection in autonomous vehicles. Automatic car always has the potential to solve traffic problems with the help of Convolution Neural Network (CNN). However, in the current scenario complete autonomy is still to be achieved. Although today's CNN have brought us closer to autonomy than ever before. CNN contain artificial neurons which are trained using preset rules and these rules determine whether it will provide an output or not when given a set of inputs. CNN will analyze various road footages, which include various scenarios such as collisions, empty roads, traffic, etc. CNN will analyze and send appropriate instructions to the car such as brake, accelerate, slow down, etc.</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":"Image Recognition by Using a Convolutional Neural Network to Identify Objects for Driverless Car","attachmentId":81448363,"attachmentType":"pdf","work_url":"https://www.academia.edu/72570607/Image_Recognition_by_Using_a_Convolutional_Neural_Network_to_Identify_Objects_for_Driverless_Car","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/72570607/Image_Recognition_by_Using_a_Convolutional_Neural_Network_to_Identify_Objects_for_Driverless_Car"><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="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></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":115901107,"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":115901107,"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_115901107" 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="90202241" 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/90202241/Vehicular_Detection_and_Classification_for_Intelligent_Transportation_System_A_Deep_Learning_Approach_Using_Faster_R_CNN_Model">Vehicular Detection and Classification for Intelligent Transportation System: A Deep Learning Approach Using Faster R-CNN Model</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="126655250" href="https://independent.academia.edu/CorazonRebong">Corazon 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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/50120086/Application_of_Deep_Learning_to_Autonomous_Robotic_Car">Application of Deep Learning to Autonomous Robotic Car</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="163841901" href="https://independent.academia.edu/AyangbekunOJ">AYANGBEKUN Oluwafemi J.</a><span>, </span><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4548671" href="https://lasunigeria.academia.edu/EngrShoewu">Engr O Shoewu, B.Sc, M.Sc, PhD</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computer Applications , 2021</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" 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class="ds-related-work--title js-related-work-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-related-work-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><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 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