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Video-based systems," /> <title>(PDF) Automated Vehicle Recognition with Deep Convolutional Neural Networks | Anuj Sharma - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/90959603/Automated_Vehicle_Recognition_with_Deep_Convolutional_Neural_Networks" /> <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 = '92477ec68c09d28ae4730a4143c926f074776319'; 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(1732827671000); window.Aedu.timeDifference = new Date().getTime() - 1732827671000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"In recent years there has been growing interest in the use of nonintrusive systems such as radar and infrared systems for vehicle recognition. State-of-the-art nonintrusive systems can report up to eight classes of vehicle types. Video-based systems, which arguably are the most popular nonintrusive detection systems, can report only very coarse classification levels (up to four classes), even with the best-performing vision systems. The present study developed a vision system that can report finer vehicle classifications according to FHWA’s scheme and is also comparable to other nonintrusive recognition systems. The proposed system decoupled object recognition into two main tasks: localization and classification. It began with localization by generating class-independent region proposals for each video frame, then it used deep convolutional neural networks to extract feature descriptors for each proposed region, and, finally, the system scored and classified the proposed regions by ...","author":[{"@context":"https://schema.org","@type":"Person","name":"Anuj Sharma"}],"contributor":[],"dateCreated":"2022-11-16","dateModified":"2022-11-16","datePublished":"2017-01-01","headline":"Automated Vehicle Recognition with Deep Convolutional Neural Networks","inLanguage":"en","keywords":["Civil Engineering","Transportation Engineering","Computer Science","Urban And Regional Planning","Operational Research","Deep Learning"],"locationCreated":null,"publication":"Transportation Research Record: Journal of the Transportation Research Board","publisher":{"@context":"https://schema.org","@type":"Organization","name":"SAGE 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State-of-the-art nonintrusive systems can report up to eight classes of vehicle types. Video-based systems, which arguably are the most popular nonintrusive detection systems, can report only very coarse classification levels (up to four classes), even with the best-performing vision systems. The present study developed a vision system that can report finer vehicle classifications according to FHWA’s scheme and is also comparable to other nonintrusive recognition systems. The proposed system decoupled object recognition into two main tasks: localization and classification. It began with localization by generating class-independent region proposals for each video frame, then it used deep convolutional neural networks to extract feature descriptors for each proposed region, and, finally, the system scored and classified the proposed regions by ...","publisher":"SAGE Publications","publication_date":"2017,,","publication_name":"Transportation Research Record: Journal of the Transportation Research Board"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Automated Vehicle Recognition with Deep Convolutional Neural Networks","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [30575540]; 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":94379609,"attachmentType":"pdf"}"><img alt="First page of “Automated Vehicle Recognition with Deep Convolutional Neural Networks”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/94379609/mini_magick20221117-1-1kf9x8g.png?1668661611" /><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">Automated Vehicle Recognition with Deep Convolutional Neural Networks</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="30575540" href="https://iastate.academia.edu/AnujSharma"><img alt="Profile image of Anuj Sharma" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Anuj Sharma</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2017, Transportation Research Record: Journal of the Transportation Research Board</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">12 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 = 90959603; 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} }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">In recent years there has been growing interest in the use of nonintrusive systems such as radar and infrared systems for vehicle recognition. State-of-the-art nonintrusive systems can report up to eight classes of vehicle types. Video-based systems, which arguably are the most popular nonintrusive detection systems, can report only very coarse classification levels (up to four classes), even with the best-performing vision systems. The present study developed a vision system that can report finer vehicle classifications according to FHWA’s scheme and is also comparable to other nonintrusive recognition systems. The proposed system decoupled object recognition into two main tasks: localization and classification. It began with localization by generating class-independent region proposals for each video frame, then it used deep convolutional neural networks to extract feature descriptors for each proposed region, and, finally, the system scored and classified the proposed regions by ...</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":94379609,"attachmentType":"pdf","workUrl":"https://www.academia.edu/90959603/Automated_Vehicle_Recognition_with_Deep_Convolutional_Neural_Networks"}">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":94379609,"attachmentType":"pdf","workUrl":"https://www.academia.edu/90959603/Automated_Vehicle_Recognition_with_Deep_Convolutional_Neural_Networks"}"><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="94379609" data-landing_url="https://www.academia.edu/90959603/Automated_Vehicle_Recognition_with_Deep_Convolutional_Neural_Networks" 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="49975663" 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/49975663/VEHICLE_CLASSIFICATION_USING_THE_CONVOLUTION_NEURAL_NETWORK_APPROACH">VEHICLE CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH</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="187691426" href="https://independent.academia.edu/MandalapuSaradaDevi">Mandalapu Sarada Devi</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="52647603" href="https://gtu-in.academia.edu/JANAKTRIVEDI">JANAK TRIVEDI</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Scientific Journal of Silesian University of Technology. Series Transport , 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">We present vehicle detection classification using the Convolution Neural Network (CNN) of the deep learning approach. The automatic vehicle classification for traffic surveillance video systems is challenging for the Intelligent Transportation System (ITS) to build a smart city. In this article, three different vehicles: bike, car and truck classification are considered for around 3,000 bikes, 6,000 cars, and 2,000 images of trucks. CNN can automatically absorb and extract different vehicle dataset's different features without a manual selection of features. The accuracy of CNN is measured in terms of the confidence values of the detected object. The highest confidence value is about 0.99 in the case of the bike category vehicle classification. The automatic vehicle classification supports building an electronic toll collection system and identifying emergency vehicles in the traffic.</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 CLASSIFICATION USING THE CONVOLUTION NEURAL NETWORK APPROACH","attachmentId":68133202,"attachmentType":"pdf","work_url":"https://www.academia.edu/49975663/VEHICLE_CLASSIFICATION_USING_THE_CONVOLUTION_NEURAL_NETWORK_APPROACH","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/49975663/VEHICLE_CLASSIFICATION_USING_THE_CONVOLUTION_NEURAL_NETWORK_APPROACH"><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="56704372" 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/56704372/An_Optimized_Approach_to_Vehicle_Type_Classification_Using_a_Convolutional_Neural_Network">An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="96272921" href="https://independent.academia.edu/NoreenKhan16">Noreen Khan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Computers, Materials & Continua</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 Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network","attachmentId":71958995,"attachmentType":"pdf","work_url":"https://www.academia.edu/56704372/An_Optimized_Approach_to_Vehicle_Type_Classification_Using_a_Convolutional_Neural_Network","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/56704372/An_Optimized_Approach_to_Vehicle_Type_Classification_Using_a_Convolutional_Neural_Network"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" 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. 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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="3" data-entity-id="100139232" 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/100139232/Vehicle_Detection_and_Classification_Using_Deep_Neural_Networks">Vehicle Detection and Classification Using Deep 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="83617235" href="https://nmsu.academia.edu/RiasatKhan">Riasat Khan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2022 International Conference on Electrical and Information Technology (IEIT)</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 Classification Using Deep Neural Networks","attachmentId":101044415,"attachmentType":"pdf","work_url":"https://www.academia.edu/100139232/Vehicle_Detection_and_Classification_Using_Deep_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/100139232/Vehicle_Detection_and_Classification_Using_Deep_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="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="5" data-entity-id="28317322" 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/28317322/Image_based_Vehicle_Analysis_using_Deep_Neural_Network_A_Systematic_Study">Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study</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="53093967" href="https://liverpool.academia.edu/ThanhToanDo">Thanh-Toan Do</a></div><p class="ds-related-work--abstract ds2-5-body-sm">—We address the vehicle detection and classification problems using Deep Neural Networks (DNNs) approaches. Here we answer to questions that are specific to our application including how to utilize DNN for vehicle detection, what features are useful for vehicle classification, and how to extend a model trained on a limited size dataset, to the cases of extreme lighting condition. Answering these questions we propose our approach that outperforms state-of-the-art methods, and achieves promising results on image with extreme lighting conditions.</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-based Vehicle Analysis using Deep Neural Network: A Systematic Study","attachmentId":48647441,"attachmentType":"pdf","work_url":"https://www.academia.edu/28317322/Image_based_Vehicle_Analysis_using_Deep_Neural_Network_A_Systematic_Study","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/28317322/Image_based_Vehicle_Analysis_using_Deep_Neural_Network_A_Systematic_Study"><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="113935455" 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/113935455/Vehicle_Type_Detection_by_Convolutional_Neural_Networks">Vehicle Type Detection by 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="32693197" href="https://uma.academia.edu/EzequielL%C3%B3pezRubio">Ezequiel López-Rubio</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Biomedical Applications Based on Natural and Artificial Computing, 2017</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 Type Detection by Convolutional Neural Networks","attachmentId":110767194,"attachmentType":"pdf","work_url":"https://www.academia.edu/113935455/Vehicle_Type_Detection_by_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/113935455/Vehicle_Type_Detection_by_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="7" data-entity-id="84937791" 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/84937791/Deep_Learning_Based_Vehicle_Classification_for_Low_Quality_Images">Deep Learning-Based Vehicle Classification for Low Quality Images</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="99717787" href="https://atilim.academia.edu/OzgenSari">Ozgen Sari</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Sensors</p><p class="ds-related-work--abstract ds2-5-body-sm">This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 × 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.</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 Classification for Low Quality Images","attachmentId":89793246,"attachmentType":"pdf","work_url":"https://www.academia.edu/84937791/Deep_Learning_Based_Vehicle_Classification_for_Low_Quality_Images","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/84937791/Deep_Learning_Based_Vehicle_Classification_for_Low_Quality_Images"><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="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="9" 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></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":94379609,"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":94379609,"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_94379609" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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