A Lightweight Traffic Sign Recognition Model Based on Improved YOLOv5

A Lightweight Traffic Sign Recognition Model Based on Improved YOLOv5 | 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 A Lightweight Traffic Sign Recognition Model Based on Improved YOLOv5 Jie Yang, Ting Sun, Wenchao Zhu, Zonghao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2857290/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 Traffic sign detection and recognition is a key technology for achieving automatic vehicle driving and maintaining road safety. The paper proposes a lightweight recognition algorithm based on YOLOv5 to address the problems of large model size, complex computation, low detection accuracy and high computational cost of existing traffic sign recognition algorithms. The algorithm is based on YOLOv5, replacing the convolutional structure in the original YOLOv5 neck network with Ghost Module and C3Ghost Module, thus reducing the redundant features in the feature fusion process, lowering the computational cost and the number of parameters; improving the PAN structure of the network and introducing the hybrid attention mechanism module CBAM, which ignores the unimportant information and capturing key information in traffic signs; adding cross-layer connections to the feature pyramid network shortens the path of information transfer, fuses more features, and improves the network feature recognition accuracy. In addition, the EIoU_Loss function is used as the bounding box regression loss function to improve the localization accuracy of the algorithm. The performance of the improved algorithm is also verified on the Chinese traffic sign dataset. The experimental results show that the improved algorithm improves the detection accuracy by 1.2% over the existing YOLOv5 algorithm, [email protected] and [email protected] :0.95 by 1.5% and 3.4% respectively, and the overall number of parameters and computational cost of the model are reduced by 14.5% and 16%. The proposed algorithm has enhanced recognition capability for targets in multiple environments and can meet the requirements for real-time traffic sign recognition. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Traffic sign detection Deep learning Attention mechanism Lightweight Full Text Additional Declarations No competing interests reported. Supplementary Files Code.zip 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-2857290","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":200259999,"identity":"b785310c-9e8e-4dea-8a3d-11c4278c44be","order_by":0,"name":"Jie Yang","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Yang","suffix":""},{"id":200260001,"identity":"ca860b7e-c476-468e-9636-2d0d2dafd6b7","order_by":1,"name":"Ting Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACNvnHBx98qLCR4+dvP/ggoaKGsBY+hrRkwxln0owlZ5xJNnhw5hhhLXIMOWbCvG2HEjccSDCTfNjCTITDGI6lMfCcOZA4s+FAWkViAxsDf3t3An4tjM3HHkhU3DHuZ248diNxhwyDxJmzG/BrYWZLNzA480wWZMuNxDNsDAYSuQS0sPGYSSS2HWYE+aUgsY2ZCC08QC0H2w4rgrQwEKdFgi3ZsAEayBIJZ47xEPSL/Azmg4//QKPy44+KGjn+9l78WjAAD2nKR8EoGAWjYBRgBQB2xFFzhX+iIwAAAABJRU5ErkJggg==","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Ting","middleName":"","lastName":"Sun","suffix":""},{"id":200260002,"identity":"3fb1f2da-e2bd-401a-aac2-cfffc50c80ac","order_by":2,"name":"Wenchao Zhu","email":"","orcid":"","institution":"Southwest Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Wenchao","middleName":"","lastName":"Zhu","suffix":""},{"id":200260003,"identity":"3ab04be9-d9cc-4c55-94dd-456f317740e2","order_by":3,"name":"Zonghao Li","email":"","orcid":"","institution":"Beijing AriTime Intelligent Control Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Zonghao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-04-25 04:29:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2857290/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2857290/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42587479,"identity":"26aa9b1d-11e3-46f9-8b42-f35767f290b2","added_by":"auto","created_at":"2023-09-04 11:52:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3813004,"visible":true,"origin":"","legend":"","description":"","filename":"LightweightTrafficSignRecognitionModelBasedonImprovedYOLOv5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2857290/v1_covered_abed536c-c841-4a16-b828-9710e3bfef3c.pdf"},{"id":37065355,"identity":"a47991c8-7a5c-4ea6-8451-440d0569a02d","added_by":"auto","created_at":"2023-05-16 05:05:34","extension":"zip","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":62629,"visible":true,"origin":"","legend":"","description":"","filename":"Code.zip","url":"https://assets-eu.researchsquare.com/files/rs-2857290/v1/5a7b63efe0528c6ab2ed72c9.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Lightweight Traffic Sign Recognition Model Based on Improved YOLOv5","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":"Traffic sign detection, Deep learning, Attention mechanism, Lightweight","lastPublishedDoi":"10.21203/rs.3.rs-2857290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2857290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Traffic sign detection and recognition is a key technology for achieving automatic vehicle driving and maintaining road safety. The paper proposes a lightweight recognition algorithm based on YOLOv5 to address the problems of large model size, complex computation, low detection accuracy and high computational cost of existing traffic sign recognition algorithms. The algorithm is based on YOLOv5, replacing the convolutional structure in the original YOLOv5 neck network with Ghost Module and C3Ghost Module, thus reducing the redundant features in the feature fusion process, lowering the computational cost and the number of parameters; improving the PAN structure of the network and introducing the hybrid attention mechanism module CBAM, which ignores the unimportant information and capturing key information in traffic signs; adding cross-layer connections to the feature pyramid network shortens the path of information transfer, fuses more features, and improves the network feature recognition accuracy. In addition, the EIoU_Loss function is used as the bounding box regression loss function to improve the localization accuracy of the algorithm. The performance of the improved algorithm is also verified on the Chinese traffic sign dataset. The experimental results show that the improved algorithm improves the detection accuracy by 1.2% over the existing YOLOv5 algorithm, [email protected] and [email protected]:0.95 by 1.5% and 3.4% respectively, and the overall number of parameters and computational cost of the model are reduced by 14.5% and 16%. 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