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hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105794127/Street_Mark_Detection_Using_Raspberry_PI_for_Self_Driving_System"><img alt="Research paper thumbnail of Street Mark Detection Using Raspberry PI for Self-Driving System" class="work-thumbnail" src="https://attachments.academia-assets.com/105163908/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105794127/Street_Mark_Detection_Using_Raspberry_PI_for_Self_Driving_System">Street Mark Detection Using Raspberry PI for Self-Driving System</a></div><div class="wp-workCard_item"><span>TELKOMNIKA (Telecommunication Computing Electronics and Control)</span><span>, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span 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data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System"><img alt="Research paper thumbnail of Street Mark Detection Using Raspberry PI for Self-driving System" class="work-thumbnail" src="https://attachments.academia-assets.com/64276389/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System">Street Mark Detection Using Raspberry PI for Self-driving System</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://uad.academia.edu/TELKOMNIKAJOURNAL">TELKOMNIKA JOURNAL</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MuhammadTaufiqurrahman60">Muhammad Taufiqurrahman</a></span></div><div class="wp-workCard_item"><span>TELKOMNIKA Telecommunication Computing Electronics and Control</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Self driving is an autonomous vehicle that can follow the road with less human intervention. The ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">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.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f7b92c518fc9bd3c03cd4b23e550dfdb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64276389,"asset_id":43950747,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64276389/download_file?st=MTczMjQ5NTUyNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="43950747"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="43950747"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43950747; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43950747]").text(description); $(".js-view-count[data-work-id=43950747]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43950747; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='43950747']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 43950747, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f7b92c518fc9bd3c03cd4b23e550dfdb" } } $('.js-work-strip[data-work-id=43950747]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":43950747,"title":"Street Mark Detection Using Raspberry PI for Self-driving System","translated_title":"","metadata":{"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. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="43950747"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System"><img alt="Research paper thumbnail of Street Mark Detection Using Raspberry PI for Self-driving System" class="work-thumbnail" src="https://attachments.academia-assets.com/64276389/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System">Street Mark Detection Using Raspberry PI for Self-driving System</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://uad.academia.edu/TELKOMNIKAJOURNAL">TELKOMNIKA JOURNAL</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/MuhammadTaufiqurrahman60">Muhammad Taufiqurrahman</a></span></div><div class="wp-workCard_item"><span>TELKOMNIKA Telecommunication Computing Electronics and Control</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Self driving is an autonomous vehicle that can follow the road with less human intervention. The ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">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.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f7b92c518fc9bd3c03cd4b23e550dfdb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":64276389,"asset_id":43950747,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/64276389/download_file?st=MTczMjQ5NTUyNCw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTUyNCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="43950747"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="43950747"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 43950747; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=43950747]").text(description); $(".js-view-count[data-work-id=43950747]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 43950747; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='43950747']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 43950747, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f7b92c518fc9bd3c03cd4b23e550dfdb" } } $('.js-work-strip[data-work-id=43950747]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":43950747,"title":"Street Mark Detection Using Raspberry PI for Self-driving System","translated_title":"","metadata":{"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. 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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":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"TELKOMNIKA Telecommunication Computing Electronics and Control"},"translated_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.","internal_url":"https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System","translated_internal_url":"","created_at":"2020-08-25T20:38:59.036-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":163561779,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":35620276,"work_id":43950747,"tagging_user_id":163561779,"tagged_user_id":30127621,"co_author_invite_id":null,"email":"s***p@gmail.com","display_order":1,"name":"sumardi undip","title":"Street Mark Detection Using Raspberry PI for Self-driving System"},{"id":35620277,"work_id":43950747,"tagging_user_id":163561779,"tagged_user_id":168946770,"co_author_invite_id":7095760,"email":"t***d@gmail.com","display_order":2,"name":"Muhammad Taufiqurrahman","title":"Street Mark Detection Using Raspberry PI for Self-driving System"},{"id":35620278,"work_id":43950747,"tagging_user_id":163561779,"tagged_user_id":null,"co_author_invite_id":7095761,"email":"m***r@undip.ac.id","display_order":3,"name":"Munawar A Riyadi","title":"Street Mark Detection Using Raspberry PI for Self-driving System"}],"downloadable_attachments":[{"id":64276389,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/64276389/thumbnails/1.jpg","file_name":"21 4509.pdf","download_url":"https://www.academia.edu/attachments/64276389/download_file?st=MTczMjQ5NTUyNCw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTUyNCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Street_Mark_Detection_Using_Raspberry_PI.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/64276389/21_4509-libre.pdf?1598414991=\u0026response-content-disposition=attachment%3B+filename%3DStreet_Mark_Detection_Using_Raspberry_PI.pdf\u0026Expires=1732499124\u0026Signature=gXrE9ZEufJW79SuoAXFBw70fDsL-3OIhDc36CelZmCvUEKriKPckzyuMYi01v7U1mmTd5xshDZft7JPJ9aw-yi9bwA7g7f04oxVlQ2ry~Yi0c7DiRcxix6gbVxDsdBg0kOHYTM0-S6bCCkWLn1hLpR7~6SHEiFVILv6hPJPd9XWhmNX41Ez1mKo56kV5sS6YnIiXM4BOpDYCZJTfTZ4Hk89NlVvMSHibgRegr5EYsVCki8Z0Y1GesqWaDo9eTBi6nVOV39I4kqpL4PbcCO2B-854N8I1WH37Yy5OSEPtO8toVVlIDdujxkEpmhQWFB65UHVL1vAUaJ-yNHVTHNa84Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System","translated_slug":"","page_count":6,"language":"en","content_type":"Work","owner":{"id":163561779,"first_name":"TELKOMNIKA","middle_initials":null,"last_name":"JOURNAL","page_name":"TELKOMNIKAJOURNAL","domain_name":"uad","created_at":"2020-07-13T20:17:20.576-07:00","display_name":"TELKOMNIKA JOURNAL","url":"https://uad.academia.edu/TELKOMNIKAJOURNAL"},"attachments":[{"id":64276389,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/64276389/thumbnails/1.jpg","file_name":"21 4509.pdf","download_url":"https://www.academia.edu/attachments/64276389/download_file?st=MTczMjQ5NTUyNCw4LjIyMi4yMDguMTQ2&st=MTczMjQ5NTUyNCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Street_Mark_Detection_Using_Raspberry_PI.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/64276389/21_4509-libre.pdf?1598414991=\u0026response-content-disposition=attachment%3B+filename%3DStreet_Mark_Detection_Using_Raspberry_PI.pdf\u0026Expires=1732499124\u0026Signature=gXrE9ZEufJW79SuoAXFBw70fDsL-3OIhDc36CelZmCvUEKriKPckzyuMYi01v7U1mmTd5xshDZft7JPJ9aw-yi9bwA7g7f04oxVlQ2ry~Yi0c7DiRcxix6gbVxDsdBg0kOHYTM0-S6bCCkWLn1hLpR7~6SHEiFVILv6hPJPd9XWhmNX41Ez1mKo56kV5sS6YnIiXM4BOpDYCZJTfTZ4Hk89NlVvMSHibgRegr5EYsVCki8Z0Y1GesqWaDo9eTBi6nVOV39I4kqpL4PbcCO2B-854N8I1WH37Yy5OSEPtO8toVVlIDdujxkEpmhQWFB65UHVL1vAUaJ-yNHVTHNa84Q__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":302887,"name":"Instrumentation and control engineering","url":"https://www.academia.edu/Documents/in/Instrumentation_and_control_engineering"}],"urls":[{"id":9090763,"url":"http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/4509"}]}, dispatcherData: dispatcherData }); 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