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<!DOCTYPE html> <html > <head> <meta charset="utf-8"> <meta rel="search" type="application/opensearchdescription+xml" href="/open_search.xml" title="Academia.edu"> <meta content="width=device-width, initial-scale=1" name="viewport"> <meta name="google-site-verification" content="bKJMBZA7E43xhDOopFZkssMMkBRjvYERV-NaN4R6mrs"> <meta name="csrf-param" content="authenticity_token" /> <meta name="csrf-token" content="Dx2ckxV15JtaUwSMZyQOejM+0GrYz4k9XBGEU954gJbMEdLVkhitmGEFcZn7U6T47bpSNfWMA/GzY/Ik/zUy8A==" /> <meta name="citation_title" content="Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm" /> <meta name="citation_publication_date" content="2022" /> <meta name="citation_journal_title" content="Journal of Advanced Transportation" /> <meta name="citation_author" content="Othman O. Khalifa" /> <meta name="citation_author" content="Muhammad H. Wajdi" /> <meta name="citation_author" content="Rashid A. 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Ahmed" /> <meta name="citation_author" content="Elmustafa Sayed Ali" /> <meta name="citation_volume" content="2022" /> <meta name="citation_firstpage" content="1-11" /> <meta name="citation_issn" content="2042-3195" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/97082644/Vehicle_Detection_for_Vision_Based_Intelligent_Transportation_Systems_Using_Convolutional_Neural_Network_Algorithm" /> <meta name="twitter:title" content="Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm" /> <meta name="twitter:description" content="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. 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Despite the" /> <title>(PDF) Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm | OTHMAN O KHALIFA and Elmustafa S A Y E D Ali - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/97082644/Vehicle_Detection_for_Vision_Based_Intelligent_Transportation_Systems_Using_Convolutional_Neural_Network_Algorithm" /> <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(1732489058000); window.Aedu.timeDifference = new Date().getTime() - 1732489058000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"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.","author":[{"@context":"https://schema.org","@type":"Person","name":"OTHMAN O KHALIFA"},{"@context":"https://schema.org","@type":"Person","name":"Elmustafa S A Y E D Ali"}],"contributor":[{"@context":"https://schema.org","@type":"Person","name":"Elmustafa S A Y E D Ali"}],"dateCreated":"2023-02-17","dateModified":null,"datePublished":null,"headline":"Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network 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"https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F97082644%2FVehicle_Detection_for_Vision_Based_Intelligent_Transportation_Systems_Using_Convolutional_Neural_Network_Algorithm%3Fshow_translation%3Dtrue"; window.loswp.previewableAttachments = [{"id":98803146,"identifier":"Attachment_98803146","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":97082644,"created_at":"2023-02-17T14:03:44.804-08:00","from_world_paper_id":null,"updated_at":"2023-06-05T02:19:00.142-07:00","_data":{"abstract":"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."},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [4676562,113737]; 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":98803146,"attachmentType":"pdf"}"><img alt="First page of “Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/98803146/mini_magick20230217-1-14200un.png?1676671461" /><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">Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm</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="4676562" href="https://redseauniveristy.academia.edu/elmustafasayed"><img alt="Profile image of Elmustafa S A Y E D Ali" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/4676562/8428112/30549807/s65_elmustafa.sayed.jpg" />Elmustafa S A Y E D Ali</a><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="113737" href="https://iium.academia.edu/OTHMANOKHALIFA"><img alt="Profile image of OTHMAN O KHALIFA" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/113737/74690625/63190936/s65_othman.khalifa.jpg" />OTHMAN O KHALIFA</a></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">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-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":98803146,"attachmentType":"pdf","workUrl":"https://www.academia.edu/97082644/Vehicle_Detection_for_Vision_Based_Intelligent_Transportation_Systems_Using_Convolutional_Neural_Network_Algorithm"}">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":98803146,"attachmentType":"pdf","workUrl":"https://www.academia.edu/97082644/Vehicle_Detection_for_Vision_Based_Intelligent_Transportation_Systems_Using_Convolutional_Neural_Network_Algorithm"}"><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="98803146" data-landing_url="https://www.academia.edu/97082644/Vehicle_Detection_for_Vision_Based_Intelligent_Transportation_Systems_Using_Convolutional_Neural_Network_Algorithm" 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="90202241" 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/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-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="126655250" href="https://independent.academia.edu/CorazonRebong">Corazon Rebong</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International journal of simulation: systems, science & technology, 2019</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":"Vehicular Detection and Classification for Intelligent Transportation System: A Deep Learning Approach Using Faster R-CNN Model","attachmentId":93829286,"attachmentType":"pdf","work_url":"https://www.academia.edu/90202241/Vehicular_Detection_and_Classification_for_Intelligent_Transportation_System_A_Deep_Learning_Approach_Using_Faster_R_CNN_Model","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/90202241/Vehicular_Detection_and_Classification_for_Intelligent_Transportation_System_A_Deep_Learning_Approach_Using_Faster_R_CNN_Model"><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="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="2" data-entity-id="43807151" 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/43807151/IJERT_Real_Time_Road_Surveillance_and_Vehicle_Detection_using_Deep_Learning">IJERT-Real Time Road Surveillance and Vehicle Detection using Deep Learning</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="109571315" href="https://independent.academia.edu/IJERTORG">IJERT Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Engineering Research and Technology (IJERT), 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">https://www.ijert.org/real-time-road-surveillance-and-vehicle-detection-using-deep-learning https://www.ijert.org/research/real-time-road-surveillance-and-vehicle-detection-using-deep-learning-IJERTV9IS070665.pdf There is a need for an intelligent transportation infrastructure and now there are technologies which could help us. Artificial Intelligence (deep learning in particular) could help with a lot of solutions to increase the efficiency of the current systems. The ability to detect and classify vehicles accurately is of paramount importance for the intelligent systems to succeed. In a country like India with growing population and limited space, these systems could play a vital role in helping us get around in the near future. Here, the focus of this project is to solve a few problems that are very relevant in the context of India. The aim is to detect and classify vehicles efficiently on a real time basis. This sets the base for further actions to be taken. For example, these actions can be detecting helmet, detecting triples, detecting seat-belt etc... (depending on the type of vehicle). This system could potentially help reduce traffic violations and also improve upon the safety of those using the road 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":"IJERT-Real Time Road Surveillance and Vehicle Detection using Deep Learning","attachmentId":64125003,"attachmentType":"pdf","work_url":"https://www.academia.edu/43807151/IJERT_Real_Time_Road_Surveillance_and_Vehicle_Detection_using_Deep_Learning","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/43807151/IJERT_Real_Time_Road_Surveillance_and_Vehicle_Detection_using_Deep_Learning"><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="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="4" data-entity-id="97048117" 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/97048117/Customized_Deep_Learning_Technique_for_Vehicle_Detection_along_with_Speed_Estimation">Customized Deep Learning Technique for Vehicle Detection along with Speed Estimation</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="29451838" href="https://vit.academia.edu/DrSagarPande">Dr. Sagar D Pande</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32919427" href="https://independent.academia.edu/IdesEditor">Grenze International Journal of Engineering and Technology GIJET</a></div><p class="ds-related-work--abstract ds2-5-body-sm">The method proposed over here in this paper is a vehicle speed estimation technique on moving vehicle under the cctv camera surveillance. For real-time vehicle detection, the YOLO (You Only Look Once) technique is employed, and the centroid approach is used to estimate vehicle speed. The video frame is converted to grayscale so that it may be processed by the computer as 0 and 1. The brightness of the scale is represented by each number. Then, by looking at these statistics, we train the YOLO Convolutional Neural Network to learn to identify the final detection. YOLO reframes object recognition as a single regression issue by taking the entire image and going directly from image pixels to bounding box coordinates and class probabilities. The next step is to compute bounding boxes (boxes that encompass the objects) using IoU (Intersect over Union) and NMS (non-maximum suppression). The IoU indicates how closely the machine's predicted bounding box fits the bounding box of the real item. However, because of the process, a problem of over-identification with a specific object arises. NMS ensures that the best cell is found among all these bounding boxes. Rather than concluding that a single car in the image has numerous causes, NMS chooses the boxes with the highest likelihood of determining the same vehicle. The vehicle centroid values are calculated after the cars have been detected. The distance traveled by vehicle is calculated using the centroid value. The speed of the vehicle is calculated after sorting out the distance that has been covered by the vehicle. YOLO is an effective and efficient strategy that epitomizes the spirit of machine learning in the suggested methodology for our vehicle recognition and speed estimation system. YOLO initially trains with 416*416 photographs, then retrains for 30 epochs at a 10-3 learning rate using 416*416 images. After training, the classifier has a top-one accuracy of 99.4% and a top-five accuracy of 99.3%.</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":"Customized Deep Learning Technique for Vehicle Detection along with Speed Estimation","attachmentId":98777846,"attachmentType":"pdf","work_url":"https://www.academia.edu/97048117/Customized_Deep_Learning_Technique_for_Vehicle_Detection_along_with_Speed_Estimation","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/97048117/Customized_Deep_Learning_Technique_for_Vehicle_Detection_along_with_Speed_Estimation"><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="122469105" 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/122469105/The_implementation_of_intelligent_systems_in_automating_vehicle_detection_on_the_road">The implementation of intelligent systems in automating vehicle detection on the road</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="178710491" href="https://universitassemarang.academia.edu/SusantoSanto">Susanto Santo</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IAES International Journal of Artificial Intelligence (IJ-AI), 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Indonesia is a country with a high population, especially in big cities. The road always crowded with various types of vehicles. Sometimes the growth of vehicles is not matched by road construction. During peak hours, too many vehicles can cause traffic jams on the road. The road is needed to be widened to accommodate the number of vehicles that pass each day. In order for road widening to be precise at locations that frequently occur in traffic jams, data on the number and classification of vehicles passing is required. Therefore, a system that can calculate and recognize the type of vehicle that passes is needed. The development of various studies on artificial intelligence especially about object detection can classify and calculate the type of vehicle. In this study, the authors used the you only look once (YOLO) object detection system using a convolution neural network (CNN) method to classify and count vehicles that pass automatically. The author uses a dataset of 600 images with 4 classes which are car, truck, bus, and motorbikes that pass through the road. The results showed that the YOLO object detection system can recognize objects consistently with accuracy more than 80% on CCTV video that installed on the road.</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":"The implementation of intelligent systems in automating vehicle detection on the road","attachmentId":117126373,"attachmentType":"pdf","work_url":"https://www.academia.edu/122469105/The_implementation_of_intelligent_systems_in_automating_vehicle_detection_on_the_road","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/122469105/The_implementation_of_intelligent_systems_in_automating_vehicle_detection_on_the_road"><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="37365242" 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/37365242/Deep_Learning_based_Vehicle_Detection_and_Tracking_Techniques_State_of_the_Art_Survey">Deep Learning based Vehicle Detection and Tracking Techniques: State-of- the-Art Survey</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="2328357" href="https://independent.academia.edu/JournalofComputerScienceIJCSIS">Journal of Computer Science IJCSIS</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Vehicle detection has become anessential task because of the rising usage of surveillance cameras in smart cities, road network managent, highway and urban traffic planning etc.But detection of vehicles faces many challenges such as occluded vehicles, shadows of structures, similarity in designs of vehicle leading to classification issues. Deep Learning based algorithms such as CNN, RCNN, Faster CNN etc. provides appropriate solution to facilitate vehicle detection because of the self learning capability of the algorithm after training. This paper aims to present an overview of various vehicle detection techniques based on deep learning which can effectively be used for video surveillance in highway, road and traffic management systems.</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 Vehicle Detection and Tracking Techniques: State-of- the-Art Survey","attachmentId":57326671,"attachmentType":"pdf","work_url":"https://www.academia.edu/37365242/Deep_Learning_based_Vehicle_Detection_and_Tracking_Techniques_State_of_the_Art_Survey","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/37365242/Deep_Learning_based_Vehicle_Detection_and_Tracking_Techniques_State_of_the_Art_Survey"><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="51054895" 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/51054895/A_Model_Based_on_Convolutional_Neural_Network_CNN_for_Vehicle_Classification">A Model Based on Convolutional Neural Network (CNN) for Vehicle 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="202459106" href="https://northsouth.academia.edu/TONMOYROY">TONMOY ROY</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="180841030" href="https://independent.academia.edu/MahdiaAmina">Mahdia Amina</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="99384668" href="https://malaya.academia.edu/FMJavedMehediShamrat">F M Javed Mehedi Shamrat</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="27426166" href="https://independent.academia.edu/KarimAsif">Joyece Jane</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">The Convolutional Neural Network (CNN) is a form of artificial neural network that has become very popular in computer vision. We proposed a convolutional neural network for classifying common types of vehicles in our country in this paper. Vehicle classification is essential in many applications, including surveillance protection systems and traffic control systems. We raised these concerns and set a goal to find a way to eliminate traffic-related road accidents. The most challenging aspect of computer vision is achieving effective outcomes in order to execute a device due to variations of data shapes and colors. We used three learning methods to identify the vehicle: MobileNetV2, DenseNet, and VGG 19, and demonstrated the methods detection accuracy. Convolutional neural networks are capable of performing all three approaches with grace. The system performs impressively on a real-time standard dataset-the Nepal dataset, which contains 4800 photographs of vehicles. DenseNet has a training accuracy of 94.32 % and a validation accuracy of 95.37%. Furthermore, the VGG 19 has a training accuracy of 91.94 % and a validation accuracy of 92.68 %. The MobileNetV2 architecture has the best accuracy, with a training accuracy of 97.01% and validation accuracy of 98.10%.</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 Model Based on Convolutional Neural Network (CNN) for Vehicle Classification","attachmentId":68916032,"attachmentType":"pdf","work_url":"https://www.academia.edu/51054895/A_Model_Based_on_Convolutional_Neural_Network_CNN_for_Vehicle_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/51054895/A_Model_Based_on_Convolutional_Neural_Network_CNN_for_Vehicle_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="8" 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="9" data-entity-id="80176070" 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/80176070/A_Comparitive_Study_of_Convolutional_Neural_Network_Models_for_Detection_Classification_and_Counting_of_Vehicles_in_Traffic">A Comparitive Study of Convolutional Neural Network Models for Detection, Classification and Counting of Vehicles in Traffic</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="45702421" href="https://independent.academia.edu/AziziAbdullah3">Azizi Abdullah</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Deep Learning based networks especially Convolutional Neural Network (CNN) models are widely used in vehicle detection, classification and counting system. On the other hand, transfer learning is a process of re-using a trained model to solve a problem similar to the one it was trained. Two ways of implementing transfer learning are direct usage of a model as a classifier and usage of a pre-trained model as a weight initialization for training with a new dataset. With recent development in the field of deep learning, many CNN models and architectures are available which makes the selection of a suitable model for performing vehicle detection, classification and counting a big challenge. Besides that, a tracking method is also required to track the vehicles in the video sequences so that the counting can be done as accurate as possible. In this project three types of CNN models i.e. SSD Inception, Faster R-CNN ResNet and Yolo DarkNet were tested on 10 traffic video samples using tran...</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 Comparitive Study of Convolutional Neural Network Models for Detection, Classification and Counting of Vehicles in Traffic","attachmentId":86645411,"attachmentType":"pdf","work_url":"https://www.academia.edu/80176070/A_Comparitive_Study_of_Convolutional_Neural_Network_Models_for_Detection_Classification_and_Counting_of_Vehicles_in_Traffic","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/80176070/A_Comparitive_Study_of_Convolutional_Neural_Network_Models_for_Detection_Classification_and_Counting_of_Vehicles_in_Traffic"><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":98803146,"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":98803146,"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_98803146" 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="112084286" 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/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-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="218588178" href="https://independent.academia.edu/TELAUMBANUAPASRAH">PASRAH 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