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window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":90202241,"created_at":"2022-11-07T07:13:28.555-08:00","from_world_paper_id":219581495,"updated_at":"2024-11-24T05:40:44.758-08:00","_data":{"publisher":"UK Simulation Society","grobid_abstract":"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.","publication_date":"2019,,","publication_name":"International journal of simulation: systems, science \u0026 technology","grobid_abstract_attachment_id":"93829286"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Vehicular Detection and Classification for Intelligent Transportation System: A Deep Learning Approach Using Faster R-CNN Model","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [126655250]; 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 ds-work-card--no-bottom-spacing"><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":93829286,"attachmentType":"pdf"}"><img alt="First page of “Vehicular Detection and Classification for Intelligent Transportation System: A Deep Learning Approach Using Faster R-CNN Model”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/93829286/mini_magick20221107-1-zvi744.png?1667834107" /><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">Vehicular Detection and Classification for Intelligent Transportation System: A Deep Learning Approach Using Faster R-CNN Model</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="126655250" href="https://independent.academia.edu/CorazonRebong"><img alt="Profile image of Corazon Rebong" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Corazon Rebong</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2019, International journal of simulation: systems, science & technology</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">7 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 = 90202241; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=90202241"; 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">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-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":93829286,"attachmentType":"pdf","workUrl":"https://www.academia.edu/90202241/Vehicular_Detection_and_Classification_for_Intelligent_Transportation_System_A_Deep_Learning_Approach_Using_Faster_R_CNN_Model"}">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":93829286,"attachmentType":"pdf","workUrl":"https://www.academia.edu/90202241/Vehicular_Detection_and_Classification_for_Intelligent_Transportation_System_A_Deep_Learning_Approach_Using_Faster_R_CNN_Model"}"><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"><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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