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window.loswp.translateUrl = "https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F101213674%2FVehicle_Control_Using_Raspberry_pi_and_Image_Processing%3Fshow_translation%3Dtrue"; window.loswp.previewableAttachments = [{"id":101815779,"identifier":"Attachment_101815779","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":101213674,"created_at":"2023-05-03T23:31:46.210-07:00","from_world_paper_id":232989046,"updated_at":"2024-11-23T06:35:04.402-08:00","_data":{"publisher":"Springer Singapore","grobid_abstract":"The objective of the proposed work is to implement the available technique to detect the stop board and red traffic signal for an autonomous car that takes action according to traffic signal with the help of raspberry pi3 board. The system also uses ultrasonic sensor for distance measurement for the purpose of speed control of vehicle to avoid collision with ahead vehicle. Rpi camera module is ued for signboard detection and ultrasonic sensors are used to get the distance information from the real world.The proposed system will get the image of the real world from the camera and then masking and contour techniques are used to detect the red signals of the traffic and To determine the traffic board signs like stop board system will use haar cascade technique to determine the stop words.So car will be able to take action and reduces the chances of human errors like driver mistakes that results road accidents .The coding for this whole system is in python and for image processing opencv is used that is much efficient as compare to the matlab .Ultrasonic sensor is used for the obstacle detection in place of camera because distance finding from the camera is more complex and computational as compare to the ultrasonic sensor. Ultrasonic sensor directly gives the obstacle distance infront of it without more complex computations.","publication_date":"2018,,","publication_name":"Intelligent Communication, Control and Devices","grobid_abstract_attachment_id":"101815779"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Vehicle Control Using Raspberry pi and Image Processing","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [251869943]; 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":101815779,"attachmentType":"pdf"}"><img alt="First page of “Vehicle Control Using Raspberry pi and Image Processing”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/101815779/mini_magick20230515-1-ccw09.png?1684128868" /><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">Vehicle Control Using Raspberry pi and Image Processing</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="251869943" href="https://independent.academia.edu/RohitTiwari226"><img alt="Profile image of Rohit Tiwari" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/251869943/105230850/94428355/s65_rohit.tiwari.jpeg" />Rohit Tiwari</a></div><p class="ds-work-card--detail ds2-5-body-sm">2018, Intelligent Communication, Control and Devices</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":101815779,"attachmentType":"pdf","workUrl":"https://www.academia.edu/101213674/Vehicle_Control_Using_Raspberry_pi_and_Image_Processing"}">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":101815779,"attachmentType":"pdf","workUrl":"https://www.academia.edu/101213674/Vehicle_Control_Using_Raspberry_pi_and_Image_Processing"}"><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="101815779" data-landing_url="https://www.academia.edu/101213674/Vehicle_Control_Using_Raspberry_pi_and_Image_Processing" 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="45041944" 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/45041944/Autonomous_Vehicle_Control_using_Image_Processing">Autonomous Vehicle Control using Image Processing</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">International Journal for Research in Applied Science & Engineering Technology, 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">A significant perspective related with vehicles is their speed. A faster vehicle encourages us to arrive at our destination in lesser time, sparing our valuable time. In any case, tragically, we have been seeing the ascent in vehicular mishaps because of uncontrolled speeding by the drivers. The traffic signs alone cannot ensure the safety of vehicles since it is dependent upon the driver to adhere to the directions. Likewise, there is the situation of human-mistake where the driver essentially neglects to pay special mind to the traffic signs. This paper focuses on the road traffic sign detection systems which help in informing the intelligent vehicle about the possible road conditions ahead and be cautious about it with the help of Image processing. A module which consists of a Raspberry Pi or USB camera with a wide view and a simple processor is installed on the vehicle. The developed system works with three different stages: image pre-processing, detection, and recognition. The entire developed system is programmed using Python incorporated with OpenCV library and implemented using open source hardware platform and open-source software environment.</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 Vehicle Control using Image Processing","attachmentId":65593343,"attachmentType":"pdf","work_url":"https://www.academia.edu/45041944/Autonomous_Vehicle_Control_using_Image_Processing","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/45041944/Autonomous_Vehicle_Control_using_Image_Processing"><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="36678585" 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/36678585/Automated_Vehicle_Collision_Avoidance_and_Navigation_Using_Raspberry_Pi">Automated Vehicle Collision Avoidance and Navigation Using Raspberry Pi</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="26204613" href="https://annauniv.academia.edu/IJETJOURNAL">International Research Group - IJET JOURNAL</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents vehicle detection and road detection is done which are adopted in intelligent vehicles. Vehicle detection and tracking is done using the Haar cascade method to identify the vehicles which are present on road. This detects the stationary as well as the movable vehicles on road. Haar like features are implemented in the system to get a clear edge of the vehicle ahead. The road detection is the free space estimation on the road in which the system in the vehicle provides a safer path to navigate. HSV method is mainly used for colour extraction. The road detection uses HSV colour space algorithm to track the real-time changes on the road. In this research vehicle detection and road detection is done at the sane time. The research process is ongoing and many algorithms are being found to reduce the problems faced on road today.</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":"Automated Vehicle Collision Avoidance and Navigation Using Raspberry Pi","attachmentId":56615754,"attachmentType":"pdf","work_url":"https://www.academia.edu/36678585/Automated_Vehicle_Collision_Avoidance_and_Navigation_Using_Raspberry_Pi","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/36678585/Automated_Vehicle_Collision_Avoidance_and_Navigation_Using_Raspberry_Pi"><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="84975556" 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/84975556/Real_Time_Traffic_Signs_and_Obstacle_Detection_in_Self_Driving_Car">Real Time Traffic Signs and Obstacle Detection in Self-Driving 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="67990035" href="https://independent.academia.edu/kondanandini">konda nandini</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Innovative Technology and Exploring Engineering</p><p class="ds-related-work--abstract ds2-5-body-sm">The motivation behind this research work is to improve car safety and efficiency.The concept of self driving cars is heard from years, it has not come into usage in many countries because of the lack of complete intelligence in the vehicle. Some of the modern vehicles provide partially automated specifications such as keeping the car within its lane, speed controls or emergency braking..According to statistics most of the accidents occur due to lack of instant response to traffic signs and obstacles ahead. In case of self driving car this problem can be addressed by detecting the traffic signals using high end camera. Real time traffic sign detection model accomplishes its objective by identifying the traffic signals and obstacles. A high end camera is used to capture the image, raspberry pi 3 is used as hardware and open computer vision library is used to process the image and identify the patterns in the image to properly detect the signals. Ultra sonic distance sensor is used to ...</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":"Real Time Traffic Signs and Obstacle Detection in Self-Driving Car","attachmentId":89819041,"attachmentType":"pdf","work_url":"https://www.academia.edu/84975556/Real_Time_Traffic_Signs_and_Obstacle_Detection_in_Self_Driving_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/84975556/Real_Time_Traffic_Signs_and_Obstacle_Detection_in_Self_Driving_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="3" data-entity-id="24545626" 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/24545626/Automated_Driving_Vehicle_Using_Image_Processing">Automated Driving Vehicle Using Image Processing</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="47315972" href="https://independent.academia.edu/MuhammadDawood20">Muhammad Dawood</a></div><p class="ds-related-work--abstract ds2-5-body-sm">—This paper focuses on control and automation of intelligent road symbol detection system for vehicles in normal environment conditions. The objective is to look for matching information or some data in the input images that are taken by the overhead mounted camera. The whole setup then filters the noise and other requirements to obtain a steady flow of information for the vehicle to be guided automatically. Here we have used MATLAB for image processing and a microcontroller interfaced with it for actual real time processing and actuation of commands. The functions attributed in the whole setup are direction control mechanism, UART Communication and MATLAB. The results obtained are shown along with neatly explained algorithm and flowchart.</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":"Automated Driving Vehicle Using Image Processing","attachmentId":44876918,"attachmentType":"pdf","work_url":"https://www.academia.edu/24545626/Automated_Driving_Vehicle_Using_Image_Processing","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/24545626/Automated_Driving_Vehicle_Using_Image_Processing"><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="73443722" 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/73443722/Image_Processing_M_L_Based_Driverless_Car_with_Image_Detection_System">Image Processing M.L Based Driverless Car with Image Detection System</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="153619710" href="https://ggathalkar.academia.edu/HimanshuTaiwade">Himanshu Taiwade</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IJARCCE</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":"Image Processing M.L Based Driverless Car with Image Detection System","attachmentId":81964095,"attachmentType":"pdf","work_url":"https://www.academia.edu/73443722/Image_Processing_M_L_Based_Driverless_Car_with_Image_Detection_System","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/73443722/Image_Processing_M_L_Based_Driverless_Car_with_Image_Detection_System"><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="88763240" 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/88763240/Advanced_Driving_Assistance_System_for_Cars_Using_Raspberry_Pi">Advanced Driving Assistance System for Cars Using Raspberry Pi</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="30919656" href="https://acharaya.academia.edu/ViswanathaV">Dr.Viswanatha V .</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Indian Journal of Science and Technology, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Objectives: Hardware implementation of advanced driving assistance system which can be able to identify i). Lane detection and assist system. ii). Blind spot detection and warning system (BSDW). iii). Forward collision and warning system (FCWS). iv). Pedestrian detection system. The primary goal of the developed system is to identify the above features in order to prevent accidents on the road and ensure pedestrian safety. Methods: The suggested method uses a canny edges detection algorithm is used to detect road edges. The input to this system is images captured by the camera with the help of the Open CV library a python image processing algorithm is created that tracks the lane. Histogram of Orientation (HOG) using the sliding window method is used for pedestrian detection. The control unit for the proposed system is Raspberry Pi module 3B, JSN-SR04T ultrasonic sensor and HC-SR04 ultrasonic sensor has been used for (BSDW) system and (FCWS) respectively. Findings: Results demonstrate that the suggested technique can accurately recognize both straight and curved lanes using an edge detection algorithm, and is also able to identify vehicles in the Blindspot area. Novelty: This technology has a high demand in the automotive industry and the system can be implemented in all future cars which can able to reduce accident rates. Keywords: Adaptive Cruise Control; Blind Spot Detection; Autonomous Driving Assistance system; Lane Detection System; Forward Collision; Pedestrian detection; OpenCV</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":"Advanced Driving Assistance System for Cars Using Raspberry Pi","attachmentId":94018201,"attachmentType":"pdf","work_url":"https://www.academia.edu/88763240/Advanced_Driving_Assistance_System_for_Cars_Using_Raspberry_Pi","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/88763240/Advanced_Driving_Assistance_System_for_Cars_Using_Raspberry_Pi"><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="49208255" 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/49208255/IJERT_Study_of_Customized_Autonomous_Car_by_using_Raspberry_Pi">IJERT-Study of Customized Autonomous Car by using Raspberry-Pi</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="109571315" href="https://independent.academia.edu/IJERTORG">IJERT Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Engineering Research and Technology (IJERT), 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">https://www.ijert.org/study-of-customized-autonomous-car-by-using-raspberry-pi https://www.ijert.org/research/study-of-customized-autonomous-car-by-using-raspberry-pi-IJERTV10IS050320.pdf In recent times, transportation has become one of the inseparable parts of human race. Advancements are made based on making cars faster and safer according to our needs. According to statistics, most road accidents take place due to lack of response time to instant traffic events. With the autonomous cars many problems faced in the traditional driving system can be effectively addressed. This can be achieved by implementing automated systems to detect traffic events happening in the real time and take necessary actions. This will transform the lifestyle of people by creating an efficient way of driving on roads autonomously. The ability to track and detect the stationary or moving objects is said to be one of the most challenging tasks for decades. To design such recognition system in self-driving automated cars, it is important to maneuver through real time traffic events. In this paper, we have reviewed some recent papers related to the topic of discussion and we have identified the required components and technologies to be used in order to navigate safely, independently, quickly and comfortably. We have proposed a system which can perform detection of lanes, obstacles, sign boards, neighbouring cars, signals, etc in near real time system. And also, working of this self-driving car prototype totally relies upon cheaper alternatives which yields better results in all the possible ways.</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":"IJERT-Study of Customized Autonomous Car by using Raspberry-Pi","attachmentId":67595021,"attachmentType":"pdf","work_url":"https://www.academia.edu/49208255/IJERT_Study_of_Customized_Autonomous_Car_by_using_Raspberry_Pi","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/49208255/IJERT_Study_of_Customized_Autonomous_Car_by_using_Raspberry_Pi"><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="77056731" 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/77056731/AUTOMATIC_SPEED_CONTROLLING_OF_VEHICLE_BASED_ON_SIGNBOARD_DETECTION_USING_IMAGE_PROCESSING">AUTOMATIC SPEED CONTROLLING OF VEHICLE BASED ON SIGNBOARD DETECTION USING IMAGE PROCESSING</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="31493941" href="https://irjet.academia.edu/IRJET">IRJET Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IRJET, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">The aim of this project is to reduce accidents and follow traffic rules by identifying and recognizing traffic sign boards in various backgrounds and lighting conditions from static digital images.These identification is done by using image processing technology. A major reason for accidents is not considering the signboards and not following the rules consequently. So to avoid this problem, introduce an automatic speed controlling vehicle using an image processing system in the vehicle which will detect the signboard. It will reduce the speed of the vehicle according to the signboard speed limit with the help of image processing algorithm and if head counts more than 10, speed of the vehicle automatically limited to 35kms/hr. Traffic sign recognition is important to the transport system on the highway road. Major approach is to detect road signs and use the data to reduce the speed of the vehicle.Proposed system will play a vital part in saving numerous lives.</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":"AUTOMATIC SPEED CONTROLLING OF VEHICLE BASED ON SIGNBOARD DETECTION USING IMAGE PROCESSING","attachmentId":84535883,"attachmentType":"pdf","work_url":"https://www.academia.edu/77056731/AUTOMATIC_SPEED_CONTROLLING_OF_VEHICLE_BASED_ON_SIGNBOARD_DETECTION_USING_IMAGE_PROCESSING","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/77056731/AUTOMATIC_SPEED_CONTROLLING_OF_VEHICLE_BASED_ON_SIGNBOARD_DETECTION_USING_IMAGE_PROCESSING"><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="43950747" 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/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System">Street Mark Detection Using Raspberry PI for Self-driving System</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="168946770" href="https://independent.academia.edu/MuhammadTaufiqurrahman60">Muhammad Taufiqurrahman</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="163561779" href="https://uad.academia.edu/TELKOMNIKAJOURNAL">TELKOMNIKA JOURNAL</a></div><p class="ds-related-work--metadata ds2-5-body-xs">TELKOMNIKA Telecommunication Computing Electronics and Control, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">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-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":"Street Mark Detection Using Raspberry PI for Self-driving System","attachmentId":64276389,"attachmentType":"pdf","work_url":"https://www.academia.edu/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System","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/43950747/Street_Mark_Detection_Using_Raspberry_PI_for_Self_driving_System"><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="9" data-entity-id="41913985" 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/41913985/A_Road_Sign_Detection_and_Recognition_Robot_using_Raspberry_Pi">A Road Sign Detection and Recognition Robot using Raspberry-Pi</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="145135674" href="https://piet.academia.edu/ArmanCodeQ">Arman CodeQ</a></div><p class="ds-related-work--abstract ds2-5-body-sm">At present situation the human beings are faced many accidents during the road ways transportation. At the same time they lose our life and valuable properties in those accidents. To avoid these problems the system designed with the help of Raspberry pi. The Digital image processing plays important role in the sign capturing and detection system. The image processing algorithms to takes the necessary action for resizing the captured signs. The Raspberry pi camera port used to capturing the road signs with image enhancement techniques. The embedded system small computing platform studies the characteristics of speed signs. In that daylight vision time to take the shape analysis for recognizing the signs using edge detection algorithms. The objective of the proposed work is to implement the available technique to traffic signal with the help of raspberry pi3 board.</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":"A Road Sign Detection and Recognition Robot using Raspberry-Pi","attachmentId":62040190,"attachmentType":"pdf","work_url":"https://www.academia.edu/41913985/A_Road_Sign_Detection_and_Recognition_Robot_using_Raspberry_Pi","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/41913985/A_Road_Sign_Detection_and_Recognition_Robot_using_Raspberry_Pi"><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></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":101815779,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":101815779,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_101815779" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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