Research on Front-Vehicle taillight recognition based on image processing and matching

Research on Front-Vehicle taillight recognition based on image processing and matching | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on Front-Vehicle taillight recognition based on image processing and matching Huayue Zhang, Junyou Zhang, Shufeng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3815314/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Taillight recognition is the key to predicting the intention of the vehicle ahead, but the current detection algorithms still have problems in small target and taillight matching. In order to solve the above problems, this paper analyzes the characteristics of automobile taillight signal, combines the image processing algorithm with the object detection technology, and puts forward a taillight signal recognition model based on image processing. Firstly, the characteristics of automobile taillight signal are analyzed, and the taillight is extracted by using HSV color space and corrosion expansion algorithm, and then the taillight is matched by artificial preset experience value to solve the problem of matching headlight. Secondly, P2 small target detection model is added to the YOLOv8s model to improve the recognition ability of small targets, and CA(coordinate attention) is inserted to reduce the interference of other light sources. Finally, EIOU Loss was added to solve the problem of sample imbalance caused by a small number of motorcycles. In this paper, the actual scene video is used for ablation experiments to verify the effectiveness of the improved algorithm. The experimental results show that the mAP value of the model is 9.3% higher than that of the YOLOv8s model. Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Autonomous driving vehicle taillights recognition Vehicle taillight matching image processing algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3815314","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":264285180,"identity":"3ebb04f5-5e68-435d-9775-f873108eede1","order_by":0,"name":"Huayue Zhang","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Huayue","middleName":"","lastName":"Zhang","suffix":""},{"id":264285182,"identity":"4f25dd2e-6c9a-4277-b0fb-91ea455eafb4","order_by":1,"name":"Junyou Zhang","email":"","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Junyou","middleName":"","lastName":"Zhang","suffix":""},{"id":264285183,"identity":"b37e66e6-6733-4c85-a35a-753aa474907c","order_by":2,"name":"Shufeng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYDACCQaGD1Am4wMGNrggXi2MM6BMZgOQFh5StLBJEKVFfnaPYcPHHbWJ/bPbr1X+KDtsb8/AfPA2D4NdHi4tBnfOGDbOPHM8ccadM2W3ec4dTuxhYEu25mFILsapRSLH/DFv27Hchhs5abcZ2w4n8DDwmEnzMBxIbMDlsBk5hs1/gVrmA7UU/mw7bM/DwP8NrxaGG0AtjG01uRtupB9j4G07zNjDwMOGV4vBjbTCxt62A/Ubb+QwS/OcS0/sOcxmbDnHIBmPw5I3NvxsqzOWu5H+8OOPMmt79vbmhzfeVNjhdhgEHAZiHgMImxlsO371QFAHxOwPCCobBaNgFIyCkQkA2OlbzUlPfnsAAAAASUVORK5CYII=","orcid":"","institution":"Shandong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Shufeng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-12-28 07:14:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3815314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3815314/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57339670,"identity":"eef9136c-fadb-488d-bd74-a44d79d8b3c6","added_by":"auto","created_at":"2024-05-29 10:14:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":904743,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonFrontVehicletaillightrecognitionbasedonimageprocessingandmatching.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3815314/v1_covered_e041d96d-0ac3-461f-836f-39484eef5b89.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Front-Vehicle taillight recognition based on image processing and matching","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autonomous driving, vehicle taillights recognition, Vehicle taillight matching, image processing algorithm","lastPublishedDoi":"10.21203/rs.3.rs-3815314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3815314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTaillight recognition is the key to predicting the intention of the vehicle ahead, but the current detection algorithms still have problems in small target and taillight matching. In order to solve the above problems, this paper analyzes the characteristics of automobile taillight signal, combines the image processing algorithm with the object detection technology, and puts forward a taillight signal recognition model based on image processing. Firstly, the characteristics of automobile taillight signal are analyzed, and the taillight is extracted by using HSV color space and corrosion expansion algorithm, and then the taillight is matched by artificial preset experience value to solve the problem of matching headlight. Secondly, P2 small target detection model is added to the YOLOv8s model to improve the recognition ability of small targets, and CA(coordinate attention) is inserted to reduce the interference of other light sources. Finally, EIOU Loss was added to solve the problem of sample imbalance caused by a small number of motorcycles. In this paper, the actual scene video is used for ablation experiments to verify the effectiveness of the improved algorithm. 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