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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="0CAIgCSQI5lB_qd0bTwirDimQiuBSD3AAW62vRB_yGjp0hHjrLnoDQi1iG6BJK9V-Ex9TKjCq-cKL62HaSBsCQ" /> <meta name="citation_title" content="Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study" /> <meta name="citation_author" content="Thanh-Toan Do" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/28317322/Image_based_Vehicle_Analysis_using_Deep_Neural_Network_A_Systematic_Study" /> <meta name="twitter:title" content="Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study" /> <meta name="twitter:description" content="—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" /> <meta name="twitter:image" content="http://a.academia-assets.com/images/twitter-card.jpeg" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/28317322/Image_based_Vehicle_Analysis_using_Deep_Neural_Network_A_Systematic_Study" /> <meta property="og:title" content="Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="—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" /> <meta property="article:author" content="https://liverpool.academia.edu/ThanhToanDo" /> <meta name="description" content="—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" /> <title>(PDF) Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study</title> <link rel="canonical" href="https://www.academia.edu/28317322/Image_based_Vehicle_Analysis_using_Deep_Neural_Network_A_Systematic_Study" /> <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 = '2b2763d861eba4fa7a8ab85b59b94c082c9d888c'; 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(1740604699000); window.Aedu.timeDifference = new Date().getTime() - 1740604699000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"—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.","author":[{"@context":"https://schema.org","@type":"Person","name":"Thanh-Toan Do","url":"https://liverpool.academia.edu/ThanhToanDo"}],"contributor":[],"dateCreated":"2016-09-07","headline":"Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study","image":"https://attachments.academia-assets.com/48647441/thumbnails/1.jpg","inLanguage":"en","keywords":["Image Processing","Machine Learning"],"publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"liverpool"}],"thumbnailUrl":"https://attachments.academia-assets.com/48647441/thumbnails/1.jpg","url":"https://www.academia.edu/28317322/Image_based_Vehicle_Analysis_using_Deep_Neural_Network_A_Systematic_Study"}</script><style 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= {"work":{"id":28317322,"created_at":"2016-09-07T09:26:17.048-07:00","from_world_paper_id":null,"updated_at":"2021-01-15T20:20:40.668-08:00","_data":{"abstract":"—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."},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [53093967]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":48647441,"attachmentType":"pdf"}"><img alt="First page of “Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/48647441/mini_magick20190202-18258-14lw3sb.png?1549180784" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study</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="53093967" href="https://liverpool.academia.edu/ThanhToanDo"><img alt="Profile image of Thanh-Toan Do" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Thanh-Toan Do</a></div><div class="ds-work-card--detail"><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">5 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 = 28317322; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=28317322"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // 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">—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-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":48647441,"attachmentType":"pdf","workUrl":"https://www.academia.edu/28317322/Image_based_Vehicle_Analysis_using_Deep_Neural_Network_A_Systematic_Study"}">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":48647441,"attachmentType":"pdf","workUrl":"https://www.academia.edu/28317322/Image_based_Vehicle_Analysis_using_Deep_Neural_Network_A_Systematic_Study"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas" data-impression-entity-id="28317322" data-impression-entity-type="2" data-impression-source="signup-banner"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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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="1" 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><p class="ds-related-work--abstract ds2-5-body-sm">Insightful transportation frameworks have recognized a proportion of consideration somewhat recently. In this space vehicle arrangement and restriction is the key errand. In this assignment the greatest test is to separate the highlights of various vehicles. Further, vehicle grouping and identification is a difficult issue to recognize and find in light of the fact that wide assortment of vehicles doesn't follow the path discipline. In this article, to distinguish and find, we have made a convolution neural organization without any preparation to group and identify objects utilizing a cutting edge Deep neural organization dependent on quick locales. In this work we have considered three kinds of vehicles like transport, vehicle and bicycle for grouping and recognition. Our methodology will utilize the whole picture as information and make a bouncing box with likelihood evaluations of the element classes as yield. The aftereffects of the investigation have shown that the projected framework can significantly improve the exactness of the discovery.</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="2" 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="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="113898235" 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/113898235/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="13930772" href="https://tuwien.academia.edu/MohsinShahzad">Mohsin Shahzad</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Applied sciences, 2023</p><p class="ds-related-work--abstract ds2-5-body-sm">This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY</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":110739713,"attachmentType":"pdf","work_url":"https://www.academia.edu/113898235/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/113898235/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="5" data-entity-id="56268895" 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/56268895/Convolutional_Neural_Network_Based_Vehicle_Classification_in_Adverse_Illuminous_Conditions_for_Intelligent_Transportation_Systems">Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems</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="7533690" href="https://independent.academia.edu/AhthashamSajid">Ahthasham Sajid</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Complexity</p><p class="ds-related-work--abstract ds2-5-body-sm">In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real-time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems","attachmentId":71737635,"attachmentType":"pdf","work_url":"https://www.academia.edu/56268895/Convolutional_Neural_Network_Based_Vehicle_Classification_in_Adverse_Illuminous_Conditions_for_Intelligent_Transportation_Systems","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/56268895/Convolutional_Neural_Network_Based_Vehicle_Classification_in_Adverse_Illuminous_Conditions_for_Intelligent_Transportation_Systems"><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="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><p class="ds-related-work--abstract ds2-5-body-sm">Intelligent Transportation System (ITS) is one of the attributes that describe smart cities. One of its functions is detection and classification of vehicles that pass through roadways. With this information, traffic management sectors can plan and implement road rules for the betterment of the traffic flow. Vision-based approaches and other methods, however, work only in ideal environment which make researchers find new ways on how limitations like occlusions, nighttime and camera angle can be solved. This paper demonstrates using a deep learning method to accurately detect and classify vehicles on urban roadways in a certain city. Additionally, a vehicle classifier was built and tested using a machine learning framework known as TensorFlow. Faster R-CNN model, with captured CCTV-video as dataset, was used to train the vehicle classifier. The performance of the newlytrained classifier has been evaluated using different classification metrics. Results show that using the proposed method, 93% accuracy and 78% F1-score in detecting and classifying vehicles were achieved based on labeled data. However, researchers also took note of the detection errors that showed during testing. Configurations in some steps has been provided to minimize such misclassifications. It was also recommended that the method be integrated as vital part of Intelligent Transportation Systems (ITS) in terms of vehicle detection and classification for future smart cities.</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="7" data-entity-id="33495873" 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/33495873/Moving_Vehicle_Detection_and_Information_Extraction_Based_on_Deep_Neural_Network">Moving Vehicle Detection and Information Extraction Based on Deep 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="37626596" href="https://dgist.academia.edu/IhsanUllahKhan">Ihsan Ullah Khan</a></div><p class="ds-related-work--abstract ds2-5-body-sm">In recent years, vehicle recognition has become an important application in intelligent traffic monitoring and management. Vehicle analysis is an essential component in many intelligent applications, such as automatic toll collection, driver assistance systems, self-guided vehicles, intelligent parking systems, and traffic statistics (vehicle count, speed, and flow). The main goal of our study is to extract the information from the moving vehicles like their make, model and type. We address the vehicle detection and recognition problems using Deep Neural Networks (DNNs) approach. Our proposed approach outperforms state-of-the-art method. We first detect the moving vehicle based on frame difference and then extract the frontal part of the vehicle based on symmetrical filter, the frontal part of the vehicle is fed into the deep architecture for recognition. The Top 1 accuracy of proposed VMMTR algorithm is 96.31%.Our method achieves promising results on image.</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":"Moving Vehicle Detection and Information Extraction Based on Deep Neural Network","attachmentId":53535636,"attachmentType":"pdf","work_url":"https://www.academia.edu/33495873/Moving_Vehicle_Detection_and_Information_Extraction_Based_on_Deep_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/33495873/Moving_Vehicle_Detection_and_Information_Extraction_Based_on_Deep_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="8" data-entity-id="87642621" 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/87642621/Comparative_Analysis_of_Deep_Learning_Models_for_Vehicle_Detection">Comparative Analysis of Deep Learning Models for Vehicle Detection</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="47939579" href="https://independent.academia.edu/RendiNurcahyo">Rendi Nurcahyo</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Systems Engineering and Information Technology (JOSEIT), 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">There are many Deep Learning model algorithms for each use case such as Object Detection which has several models, including the ones commonly used, namely Faster R-CNN, SSD (Single Shot Detector), and YOLO (You Only Look Once) version 3, but we need to know the best model for Object Detection especially for vehicle detection which will used for surveillance system. From these models we want to compare which model is the best in a real time process. Each Deep Learning model has its own advantages and disadvantages that affect its performance. Therefore, we must determine which model fits our use case and dataset in order to produce the model that has the best performance. Based on these needs, this paper will make a comparative analysis of the Deep Learning model for Vehicle Detection of these models, namely Faster R-CNN, SSD, and YOLO v3 to see the advantages and disadvantages and which one is the best. The parameters used for comparison are MAP, FPS, Latency which represent whether the model is suitable for real time or not. After comparisons were made, it was concluded that of the three models mentioned, only the YOLO v3 model could be used as real time detection because it had low latency. Its inference time is 60% faster than SSD and 85% faster than Faster R-CNN, furthermore YOLO v3 only carried out a single convolution process, making the process simpler and faster without reducing its accuracy.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Comparative Analysis of Deep Learning Models for Vehicle Detection","attachmentId":91796036,"attachmentType":"pdf","work_url":"https://www.academia.edu/87642621/Comparative_Analysis_of_Deep_Learning_Models_for_Vehicle_Detection","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/87642621/Comparative_Analysis_of_Deep_Learning_Models_for_Vehicle_Detection"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="90641130" 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/90641130/A_Novel_Vehicle_Classification_Model_for_Urban_Traffic_Surveillance_Using_the_Deep_Neural_Network_Model">A Novel Vehicle Classification Model for Urban Traffic Surveillance Using the Deep Neural Network 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="6065196" href="https://independent.academia.edu/kaurdapinder">dapinder kaur</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Education and Management Engineering, 2016</p><p class="ds-related-work--abstract ds2-5-body-sm">The vehicle detection is the backbone of the urban surveillance systems, which is used to obtain and identify the various statistics of the urban vehicular mobility. Also the urban surveillance systems are used for the vehicle tracking or vehicular object classification. The proposed model has been designed for the purpose of the urban surveillance and vehicular modelling of the traffic. The proposed model has been designed for the vehicle position identification as well as the vehicle type classification using the deep neural network. The proposed model has been tested with a standard dataset image for the result evaluation. The experimental results has been shown the effectiveness of the proposed model, where the proposed model has been found successful in detection and classification of all of the vehicles in the given image.</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 Novel Vehicle Classification Model for Urban Traffic Surveillance Using the Deep Neural Network Model","attachmentId":94147965,"attachmentType":"pdf","work_url":"https://www.academia.edu/90641130/A_Novel_Vehicle_Classification_Model_for_Urban_Traffic_Surveillance_Using_the_Deep_Neural_Network_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/90641130/A_Novel_Vehicle_Classification_Model_for_Urban_Traffic_Surveillance_Using_the_Deep_Neural_Network_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></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":48647441,"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":48647441,"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_48647441" 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="115285968" 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/115285968/Fast_and_Accurate_Deep_Learning_Architecture_on_Vehicle_Type_Recognition">Fast and Accurate Deep Learning Architecture on Vehicle Type Recognition</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="4100949" href="https://mahasarakham.academia.edu/OlarikSurinta">Olarik Surinta</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Current Applied Science and Technology, 2021</p><div 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Jane</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE, 2021</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-related-work-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 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