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(PDF) Lane Detection and Traffic Sign Recognition using OpenCV and Deep Learning for Autonomous Vehicles | IRJET Journal - Academia.edu
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Major benefits of automated vehicles include a 90% reduction in traffic deaths, a 60%" /> <title>(PDF) Lane Detection and Traffic Sign Recognition using OpenCV and Deep Learning for Autonomous Vehicles | IRJET Journal - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/86387247/Lane_Detection_and_Traffic_Sign_Recognition_using_OpenCV_and_Deep_Learning_for_Autonomous_Vehicles" /> <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(1732810624000); window.Aedu.timeDifference = new Date().getTime() - 1732810624000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"By the means of automation, number of car crashes on road can be reduced. Testing of autonomous vehicles on public roads can be done on the public roads of the US. Major benefits of automated vehicles include a 90% reduction in traffic deaths, a 60% reduction in harmful emissions, a 40% reduction in travel time, and a 500% increase in lane capacity. Autonomous vehicles are expected to be safer. \u0026amp;quot;Over 90% of accidents today are caused by driver error,\u0026amp;quot; said Professor Robert W.Peterson. Autonomous cars are designed for the elimination of traffic created by stop-and-go behavior, according to research done at the University of Illinois. This will be helpful in saving the time of people and as well decreases the time their cars are on the roads, which will reduce the emission of harmful gases from the vehicles. A very close part of driver assistant systems is lane detection. Lane detection refers to the process of tracing white markings on the road, capturing and processing images using a camera mounted in front of the car, and this is done using the OpenCV library. Safety driving also involves recognition of traffic signs as a major part. Promising results have been presented by the CNN (convolutional neural networks).","author":[{"@context":"https://schema.org","@type":"Person","name":"IRJET Journal"}],"contributor":[],"dateCreated":"2022-09-09","dateModified":"2022-09-09","datePublished":"2022-01-01","headline":"Lane Detection and Traffic Sign Recognition using OpenCV and Deep Learning for Autonomous Vehicles","inLanguage":"en","keywords":["Engineering"],"locationCreated":null,"publication":"IRJET","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/86387247/Lane_Detection_and_Traffic_Sign_Recognition_using_OpenCV_and_Deep_Learning_for_Autonomous_Vehicles","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":"irjet"}]}</script><link rel="stylesheet" media="all" 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automation, number of car crashes on road can be reduced. Testing of autonomous vehicles on public roads can be done on the public roads of the US. Major benefits of automated vehicles include a 90% reduction in traffic deaths, a 60% reduction in harmful emissions, a 40% reduction in travel time, and a 500% increase in lane capacity. Autonomous vehicles are expected to be safer. \"Over 90% of accidents today are caused by driver error,\" said Professor Robert W.Peterson. Autonomous cars are designed for the elimination of traffic created by stop-and-go behavior, according to research done at the University of Illinois. This will be helpful in saving the time of people and as well decreases the time their cars are on the roads, which will reduce the emission of harmful gases from the vehicles. A very close part of driver assistant systems is lane detection. Lane detection refers to the process of tracing white markings on the road, capturing and processing images using a camera mounted in front of the car, and this is done using the OpenCV library. Safety driving also involves recognition of traffic signs as a major part. Promising results have been presented by the CNN (convolutional neural networks).","publication_date":"2022,,","publication_name":"IRJET"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Lane Detection and Traffic Sign Recognition using OpenCV and Deep Learning for Autonomous Vehicles","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [31493941]; 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":90852294,"attachmentType":"pdf"}"><img alt="First page of “Lane Detection and Traffic Sign Recognition using OpenCV and Deep Learning for Autonomous Vehicles”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/90852294/mini_magick20220910-1-1jcrwqk.png?1662790535" /><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">Lane Detection and Traffic Sign Recognition using OpenCV and Deep Learning for Autonomous Vehicles</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="31493941" href="https://irjet.academia.edu/IRJET"><img alt="Profile image of IRJET Journal" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/31493941/9304077/11813823/s65_irjet.journal.jpg" />IRJET Journal</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2022, IRJET</p><a class="js-loswp-work-card-doi-link ds2-5-body-sm ds2-5-body-link" href="https://doi.org/10.1016/j.proeng.2017.09.594." rel="nofollow">https://doi.org/10.1016/j.proeng.2017.09.594.</a><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">3 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 = 86387247; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=86387247"; 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) => { const viewCountNumber = Number(viewCount); if (!viewCountNumber) { throw new Error('Failed to parse view count'); } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (viewCountBody) { viewCountBody.textContent = `${commaizedViewCount} views`; } else { throw new Error('Failed to find work views element'); } }; // 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">By the means of automation, number of car crashes on road can be reduced. Testing of autonomous vehicles on public roads can be done on the public roads of the US. Major benefits of automated vehicles include a 90% reduction in traffic deaths, a 60% reduction in harmful emissions, a 40% reduction in travel time, and a 500% increase in lane capacity. Autonomous vehicles are expected to be safer. "Over 90% of accidents today are caused by driver error," said Professor Robert W.Peterson. Autonomous cars are designed for the elimination of traffic created by stop-and-go behavior, according to research done at the University of Illinois. This will be helpful in saving the time of people and as well decreases the time their cars are on the roads, which will reduce the emission of harmful gases from the vehicles. A very close part of driver assistant systems is lane detection. Lane detection refers to the process of tracing white markings on the road, capturing and processing images using a camera mounted in front of the car, and this is done using the OpenCV library. Safety driving also involves recognition of traffic signs as a major part. Promising results have been presented by the CNN (convolutional neural networks).</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":90852294,"attachmentType":"pdf","workUrl":"https://www.academia.edu/86387247/Lane_Detection_and_Traffic_Sign_Recognition_using_OpenCV_and_Deep_Learning_for_Autonomous_Vehicles"}">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":90852294,"attachmentType":"pdf","workUrl":"https://www.academia.edu/86387247/Lane_Detection_and_Traffic_Sign_Recognition_using_OpenCV_and_Deep_Learning_for_Autonomous_Vehicles"}"><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="90852294" data-landing_url="https://www.academia.edu/86387247/Lane_Detection_and_Traffic_Sign_Recognition_using_OpenCV_and_Deep_Learning_for_Autonomous_Vehicles" 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="67600472" 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/67600472/Use_of_Machine_Learning_in_Automobile_Industry_to_Improve_Safety_Using_CNN">Use of Machine Learning in Automobile Industry to Improve Safety Using CNN</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="6079060" href="https://independent.academia.edu/IJRASETPublication">IJRASET Publication</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IJRASET, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Vision-based vehicle steering system cars can have three main roles: 1) road access; 2) an obstacle to find; and 3) signal recognition. The first two have already been taught many years and there have been many positive results, but a sign of traffic recognition is a less readable field. Road signs provide drivers with the most important information on the road, to do driving is safe and easy. We think road signs should play the same role of private cars. The color and shape are very different from the natural environment. The algorithm described in this paper uses this feature. It has two main parts. The first, to find, uses color range to separate image analysis and shapes to get symptoms. The second, in stages, uses the neural network. Some effects from natural forums are shown. On the other hand, the algorithm works to detect other types of marks can tell a moving robot to perform a specific task that place.</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":"Use of Machine Learning in Automobile Industry to Improve Safety Using CNN","attachmentId":78358537,"attachmentType":"pdf","work_url":"https://www.academia.edu/67600472/Use_of_Machine_Learning_in_Automobile_Industry_to_Improve_Safety_Using_CNN","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/67600472/Use_of_Machine_Learning_in_Automobile_Industry_to_Improve_Safety_Using_CNN"><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="72532830" 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/72532830/Lane_Detection_System_in_Autonomous_Cars_Powered_by_Image_Processing_and_Artificial_Intelligence">Lane Detection System in Autonomous Cars Powered by Image Processing and Artificial Intelligence</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="95270980" href="https://erbakan.academia.edu/tlhcelik">Talha Çelik</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Engineering Science and Computing, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">In this study, a deep learning-based model has been proposed so that highway vehicles with autonomous driving capability can recognize the lanes they are in and determine their position on the highway while driving. 7000 highway images with different weather conditions were used to create this deep learning model. During the model training, the values of the training parameters were renewed, experiments were made and the error rate of the created model was tried to be minimized. As a result, in this proposed deep learning-based model, lanes on the highway were detected with an error rate of 0.09%.</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":"Lane Detection System in Autonomous Cars Powered by Image Processing and Artificial Intelligence","attachmentId":81423937,"attachmentType":"pdf","work_url":"https://www.academia.edu/72532830/Lane_Detection_System_in_Autonomous_Cars_Powered_by_Image_Processing_and_Artificial_Intelligence","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/72532830/Lane_Detection_System_in_Autonomous_Cars_Powered_by_Image_Processing_and_Artificial_Intelligence"><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="50280953" 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/50280953/Development_of_a_Vehicle_for_Driving_with_Convolutional_Neural_Network">Development of a Vehicle for Driving with 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="45362177" href="https://independent.academia.edu/ArbnorPajaziti">Arbnor Pajaziti</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Advanced Computer Science and 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":"Development of a Vehicle for Driving with Convolutional Neural Network","attachmentId":68325483,"attachmentType":"pdf","work_url":"https://www.academia.edu/50280953/Development_of_a_Vehicle_for_Driving_with_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/50280953/Development_of_a_Vehicle_for_Driving_with_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="3" data-entity-id="107692729" 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/107692729/Implementation_of_Low_cost_Autonomous_Car_for_Lane_Recognition_and_Keeping_based_on_Deep_Neural_Network_model">Implementation of Low-cost Autonomous Car for Lane Recognition and Keeping based on 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="658892" href="https://independent.academia.edu/MihwaSong">Mi-hwa Song</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2021</p><p class="ds-related-work--abstract ds2-5-body-sm">CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping</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":"Implementation of Low-cost Autonomous Car for Lane Recognition and Keeping based on Deep Neural Network model","attachmentId":106288105,"attachmentType":"pdf","work_url":"https://www.academia.edu/107692729/Implementation_of_Low_cost_Autonomous_Car_for_Lane_Recognition_and_Keeping_based_on_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/107692729/Implementation_of_Low_cost_Autonomous_Car_for_Lane_Recognition_and_Keeping_based_on_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 class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="96986263" 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/96986263/Lane_Detection_in_Autonomous_Vehicles_A_Systematic_Review">Lane Detection in Autonomous Vehicles: A Systematic Review</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="228463331" href="https://independent.academia.edu/DRNURBAITIWAHID">DR. NURBAITI WAHID</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IEEE Access</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":"Lane Detection in Autonomous Vehicles: A Systematic Review","attachmentId":98732793,"attachmentType":"pdf","work_url":"https://www.academia.edu/96986263/Lane_Detection_in_Autonomous_Vehicles_A_Systematic_Review","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/96986263/Lane_Detection_in_Autonomous_Vehicles_A_Systematic_Review"><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="108887406" 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/108887406/Lane_Line_Detection_via_Deep_Learning_Based_Approach_Applying_Two_Types_of_Input_into_Network_Model">Lane Line Detection via Deep Learning Based- Approach Applying Two Types of Input into 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="102127216" href="https://teknologimalaysia.academia.edu/jannahzakaria">jannah zakaria</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of the Society of Automotive Engineers Malaysia</p><p class="ds-related-work--abstract ds2-5-body-sm">Lane line detection is one of the important modules for Advanced Driver-Assistance System (ADAS) that are applied in the autonomous vehicle. This module work by exhibit the position of the road lane marking and providing the details of the geometrical features of the lane line structures into the intelligent system. This paper proposes the lane line marking detection using Fully Convolutional Neural Network (FCN) model by investigating the two types of input fed into the networks. RGB- channel (Red, Green, Blue) and Canny edge were used as the inputs to develop in the FCN model. The FCN approach has been proposed as one of the solution methods in mitigating the road lane detection issues due to its great performance in the application of objects detection in image or video. Previously, the RGB-channel is widely applied in the deep learning method meanwhile, the Canny-edge input has not been applied yet in the deep learning method. Therefore, this study investigates the further perfo...</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":"Lane Line Detection via Deep Learning Based- Approach Applying Two Types of Input into Network Model","attachmentId":107159883,"attachmentType":"pdf","work_url":"https://www.academia.edu/108887406/Lane_Line_Detection_via_Deep_Learning_Based_Approach_Applying_Two_Types_of_Input_into_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/108887406/Lane_Line_Detection_via_Deep_Learning_Based_Approach_Applying_Two_Types_of_Input_into_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 class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="118512246" 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/118512246/Autonomous_Road_Sign_Recognition_and_Lane_Detection_Using_Convolutional_Neural_Networks">Autonomous Road Sign Recognition and Lane Detection Using 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="269493191" href="https://independent.academia.edu/TejasShinde112">Tejas Shinde</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Approximately 1.35 million people die each year as a result of road traffic crashes, and between 40 to 70 million are injured drastically. Most of these accidents takes place due to lack of response time to instant traffic events. To design such recognition and detection system in autonomous cars, it is important to monitor and guide through real time traffic events. This involves 1) Road sign recognition 2) Road lane detection. Road sign recognition have been studied for many years and with many good results, but road lane detection is a lessstudied field. Road lane detection provide drivers with very valuable information about which lane they are following and any possible lane departure, in order to make driving safer and easier. In this paper, an attempt is made to develop such system, by applying image recognition to capture traffic signs, classify and process them correctly using Convolutional Neural Network. Index Terms Computer Vision, Canny Detection, Neural Networks, Gauss...</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":"Autonomous Road Sign Recognition and Lane Detection Using Convolutional Neural Networks","attachmentId":114120182,"attachmentType":"pdf","work_url":"https://www.academia.edu/118512246/Autonomous_Road_Sign_Recognition_and_Lane_Detection_Using_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/118512246/Autonomous_Road_Sign_Recognition_and_Lane_Detection_Using_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="50120086" 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/50120086/Application_of_Deep_Learning_to_Autonomous_Robotic_Car">Application of Deep Learning to Autonomous Robotic Car</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="163841901" href="https://independent.academia.edu/AyangbekunOJ">AYANGBEKUN Oluwafemi J.</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4548671" href="https://lasunigeria.academia.edu/EngrShoewu">Engr O Shoewu, B.Sc, M.Sc, PhD</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computer Applications , 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Autonomous machines are becoming prevalent, even more so the advent of autonomous vehicles. While autonomous cars have been around for some time, the endless innovations in this domain have led to the removal of human-in-the loop, hence constantly seeking to remove human input while delivering optimal result. However, safety is a major concern, and users are wary of leaving safety level decisions to machines. There is a rise in road accident caused by autonomous cars, while some have blamed it on human's total trust in machines, and researchers have called for the development of human-level accurate algorithms to tackle decision making using state-of-the-art techniques. Therefore, this paper seeks to use computer vision leveraging on deep learning techniques to detect pedestrians, traffic signs, important objects, and lane lines to infer crucial driver decisions.Mask R-Convolutional Neural Network (CNN) was used for object classification with the aid of transfer learning saving the hassle of training and GPU times. A simple method for collecting data was applied using a wide-anglecamera and using Google TPU to perform real time object recognition without the need for a GPU enabled machine.</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":"Application of Deep Learning to Autonomous Robotic Car","attachmentId":68224478,"attachmentType":"pdf","work_url":"https://www.academia.edu/50120086/Application_of_Deep_Learning_to_Autonomous_Robotic_Car","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/50120086/Application_of_Deep_Learning_to_Autonomous_Robotic_Car"><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="95344669" 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/95344669/Analysis_of_Lane_Detection_Techniques_on_Structured_Roads_using_OpenCV">Analysis of Lane Detection Techniques on Structured Roads using OpenCV</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="254101520" href="https://independent.academia.edu/AkashPunagin">Akash Punagin</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal for Research in Applied Science and Engineering Technology, 2020</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" 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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/39574850/Lane_detection_method_based_on_lane_structural_analysis_and_CNNs">Lane detection method based on lane structural analysis and CNNs</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="116960113" href="https://upds.academia.edu/AmulSingh">Amul Singh</a></div><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":"Lane detection method based on lane structural analysis and CNNs","attachmentId":59728424,"attachmentType":"pdf","work_url":"https://www.academia.edu/39574850/Lane_detection_method_based_on_lane_structural_analysis_and_CNNs","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" 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