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This has become the core of most state of art computer vision solutions for wide variety of tasks." /> <title>(PDF) Vehicle Make and Model Classification Using Convolutional Neural Networks | Syed Hasib Akhter Faruqui - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/33300656/Vehicle_Make_and_Model_Classification_Using_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(1732827808000); window.Aedu.timeDifference = new Date().getTime() - 1732827808000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"In recent times, for the purpose of object detection in large-scale generic database, Deep Convolution Neural Network (D-CNN) has gained popularity. This has become the core of most state of art computer vision solutions for wide variety of tasks. The main aim of this report is to detect the make and model of the vehicals. This is important in the area of traffic control management, identification, stolen APB etc. In this project, state of art Convolution Network along with one of our own constructed network are compared. The architecture of our network is a deep convolution network with very small convolution filter, which showed a significant improvement in computation. 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Network (D-CNN) has gained popularity. This has become the core of most state of art computer vision solutions for wide variety of tasks. The main aim of this report is to detect the make and model of the vehicals. This is important in the area of traffic control management, identification, stolen APB etc. In this project, state of art Convolution Network along with one of our own constructed network are compared. The architecture of our network is a deep convolution network with very small convolution filter, which showed a significant improvement in computation. 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This has become the core of most state of art computer vision solutions for wide variety of tasks. The main aim of this report is to detect the make and model of the vehicals. This is important in the area of traffic control management, identification, stolen APB etc. In this project, state of art Convolution Network along with one of our own constructed network are compared. The architecture of our network is a deep convolution network with very small convolution filter, which showed a significant improvement in computation. Although, GoogleNet Net shows a significant accuracy in classification compared to our model.</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":53365961,"attachmentType":"pdf","workUrl":"https://www.academia.edu/33300656/Vehicle_Make_and_Model_Classification_Using_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":53365961,"attachmentType":"pdf","workUrl":"https://www.academia.edu/33300656/Vehicle_Make_and_Model_Classification_Using_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="53365961" data-landing_url="https://www.academia.edu/33300656/Vehicle_Make_and_Model_Classification_Using_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="33495877" 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/33495877/Vehicle_Make_and_Model_Recognition_System_based_on_Convolutional_Neural_Network_Vehicle_Make_and_Model_Recognition_System_based_on_Convolutional_Neural_Network">Vehicle Make and Model Recognition System based on Convolutional Neural Network Vehicle Make and Model Recognition System based on 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="37626596" href="https://dgist.academia.edu/IhsanUllahKhan">Ihsan Ullah Khan</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Vehicles make and model recognition (VMMR) is important for vehicles analysis. A real-time Vehicle Make and Model Recognition (VMMR) system is a significant component of security applications in Intelligent Transportation Systems (ITS). A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. In this paper we present a VMMR based on deep neural network(DNN). Experimental results shows that our deep learning based VMMR achieved high accuracy in real time.</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 Make and Model Recognition System based on Convolutional Neural Network Vehicle Make and Model Recognition System based on Convolutional Neural Network","attachmentId":53535640,"attachmentType":"pdf","work_url":"https://www.academia.edu/33495877/Vehicle_Make_and_Model_Recognition_System_based_on_Convolutional_Neural_Network_Vehicle_Make_and_Model_Recognition_System_based_on_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/33495877/Vehicle_Make_and_Model_Recognition_System_based_on_Convolutional_Neural_Network_Vehicle_Make_and_Model_Recognition_System_based_on_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="1" 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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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. 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