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(PDF) Street Mark Detection Using Raspberry PI for Self-driving System
<!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="ei9XiDv2YSB3GrAwJANXnsKC0fveiHUBEUHMAToY4rUfqz48dkCnvDsMUva79r9_jBjHwmPC9RvdcyLL-qgUsg" /> <meta name="citation_title" content="Street Mark Detection Using Raspberry PI for Self-driving System" /> <meta name="citation_publication_date" content="2018/01/01" /> <meta name="citation_journal_title" content="TELKOMNIKA Telecommunication Computing Electronics and Control" /> <meta name="citation_author" content="TELKOMNIKA JOURNAL" /> <meta name="citation_author" content="Muhammad Taufiqurrahman" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:url" content="https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System" /> <meta name="twitter:title" content="Street Mark Detection Using Raspberry PI for Self-driving System" /> <meta name="twitter:description" content="Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection" /> <meta name="twitter:image" content="https://0.academia-photos.com/163561779/45937815/35658545/s200_telkomnika.journal.png" /> <meta property="fb:app_id" content="2369844204" /> <meta property="og:type" content="article" /> <meta property="og:url" content="https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System" /> <meta property="og:title" content="Street Mark Detection Using Raspberry PI for Self-driving System" /> <meta property="og:image" content="http://a.academia-assets.com/images/open-graph-icons/fb-paper.gif" /> <meta property="og:description" content="Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection" /> <meta property="article:author" content="https://uad.academia.edu/TELKOMNIKAJOURNAL" /> <meta property="article:author" content="https://independent.academia.edu/MuhammadTaufiqurrahman60" /> <meta name="description" content="Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection" /> <title>(PDF) Street Mark Detection Using Raspberry PI for Self-driving System</title> <link rel="canonical" href="https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System" /> <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 = 'b092bf3a3df71cf13feee7c143e83a57eb6b94fb'; 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(1739841920000); window.Aedu.timeDifference = new Date().getTime() - 1739841920000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection system was designed using webcam and raspberry-pi mini computer for processing the image. The image was processed by HSV color filtering method. The processing rate of this algorithm was 137.98 ms correspondinig to 7.2 FPS. The self-driving prototype was found to be working optimally for \u0026amp;quot;hue\u0026amp;quot; threshold of 0-179, \u0026amp;quot;saturation\u0026amp;quot; threshold of 0-30, and \u0026amp;quot;value\u0026amp;quot; threshold of 200-255. Street mark detection has been obtained from the coordinates of street mark object which had range 4-167 on x axis and 4-139 on y axis. As a result, we have successfully built the street mark detection by COG method more effectively and smoothly in detection in comparison with Hough transform method.","author":[{"@context":"https://schema.org","@type":"Person","name":"TELKOMNIKA JOURNAL","url":"https://uad.academia.edu/TELKOMNIKAJOURNAL"},{"@context":"https://schema.org","@type":"Person","name":"Muhammad Taufiqurrahman","url":"https://independent.academia.edu/MuhammadTaufiqurrahman60"}],"contributor":[{"@context":"https://schema.org","@type":"Person","name":"Muhammad Taufiqurrahman","url":"https://independent.academia.edu/MuhammadTaufiqurrahman60"}],"dateCreated":"2020-08-25","dateModified":"2020-09-04","datePublished":"2018-01-01","headline":"Street Mark Detection Using Raspberry PI for Self-driving 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{"work":{"id":43950747,"created_at":"2020-08-25T20:38:59.036-07:00","from_world_paper_id":null,"updated_at":"2022-11-29T17:42:52.088-08:00","_data":{"doi":"10.12928/telkomnika.v16i2.4509","issue":"2","volume":"16","abstract":"Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection system was designed using webcam and raspberry-pi mini computer for processing the image. The image was processed by HSV color filtering method. The processing rate of this algorithm was 137.98 ms correspondinig to 7.2 FPS. The self-driving prototype was found to be working optimally for \"hue\" threshold of 0-179, \"saturation\" threshold of 0-30, and \"value\" threshold of 200-255. Street mark detection has been obtained from the coordinates of street mark object which had range 4-167 on x axis and 4-139 on y axis. As a result, we have successfully built the street mark detection by COG method more effectively and smoothly in detection in comparison with Hough transform method.","page_numbers":"629-634","publication_date":"2018,,","publication_name":"TELKOMNIKA Telecommunication Computing Electronics and Control"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Street Mark Detection Using Raspberry PI for Self-driving System","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [163561779,168946770]; 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":64276389,"attachmentType":"pdf"}"><img alt="First page of “Street Mark Detection Using Raspberry PI for Self-driving System”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/64276389/mini_magick20200825-32044-a9hv5k.png?1598415370" /><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">Street Mark Detection Using Raspberry PI for Self-driving System</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="163561779" href="https://uad.academia.edu/TELKOMNIKAJOURNAL"><img alt="Profile image of TELKOMNIKA JOURNAL" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/163561779/45937815/35658545/s65_telkomnika.journal.png" />TELKOMNIKA JOURNAL</a><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="168946770" href="https://independent.academia.edu/MuhammadTaufiqurrahman60"><img alt="Profile image of Muhammad Taufiqurrahman" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/168946770/127204212/116586055/s65_muhammad.taufiqurrahman.png" />Muhammad Taufiqurrahman</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2018, TELKOMNIKA Telecommunication Computing Electronics and Control</p><a class="js-loswp-work-card-doi-link ds2-5-body-sm ds2-5-body-link" href="https://doi.org/10.12928/telkomnika.v16i2.4509" rel="nofollow">https://doi.org/10.12928/telkomnika.v16i2.4509</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">6 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 = 43950747; 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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">Self driving is an autonomous vehicle that can follow the road with less human intervention. The development of self driving utilizes various methods such as radar, lidar, GPS, camera, or combination of them. In this research, street mark detection system was designed using webcam and raspberry-pi mini computer for processing the image. The image was processed by HSV color filtering method. The processing rate of this algorithm was 137.98 ms correspondinig to 7.2 FPS. The self-driving prototype was found to be working optimally for "hue" threshold of 0-179, "saturation" threshold of 0-30, and "value" threshold of 200-255. Street mark detection has been obtained from the coordinates of street mark object which had range 4-167 on x axis and 4-139 on y axis. As a result, we have successfully built the street mark detection by COG method more effectively and smoothly in detection in comparison with Hough transform method.</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":64276389,"attachmentType":"pdf","workUrl":"https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System"}">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":64276389,"attachmentType":"pdf","workUrl":"https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System"}"><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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