Small-target traffic sign detection based on improved YOLOv8

Small-target traffic sign detection based on improved YOLOv8 | 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 Research Article Small-target traffic sign detection based on improved YOLOv8 Yujiao Ma, Bin Gao, Linlin Li, Yutong Li, Shutian Liu, Zhengjun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5311257/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 To address the challenges of high proportions of small traffic signs and significant environmental interference in road traffic scenarios, an improved YOLOv8 network specifically designed for small traffic sign detection is proposed. We first integrated BRA based on the Transformer architecture into the C2f network structure of YOLOv8n. This integration aims to reduce missed detections of small objects and improve the network’s perception of small targets. In addition, CARAFE was employed to retain complex image feature information and reduce the loss of target information without significantly increasing model complexity and parameters. This approach enhances the model’s accuracy and generalization capability. The inner-IOU loss function based on auxiliary bounding boxes was also introduced. The improved method was trained on the public traffic sign dataset CCTSDB, achieving a detection accuracy of 97.5%, which is 1.8% more than the original algorithm, and it also increases detection speed by 11 FPS. Experimental results indicate that the improved YOLOv8 network effectively detects traffic signs in complex road scenarios. YOLOv8 Small target detection BRA CARAFE Complex road scenes 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. 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-5311257","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373363788,"identity":"02937b46-b9d3-4db0-a17a-ae2c4ce24915","order_by":0,"name":"Yujiao Ma","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Yujiao","middleName":"","lastName":"Ma","suffix":""},{"id":373363789,"identity":"6946e950-b1d1-46f4-b36b-a1a576126550","order_by":1,"name":"Bin Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYHACNoYEBpsEPoYDYDbRWtIS2EjTwsBwOIENziYE5N0PH3vwcMf5PDbGMwYMH8oOM/DPbsCvxfBMWrpB4pnbxWwMZwwYZ5w7zCBx5wABLQ05ZhKJbbcT24BamHnbDjMYSCQQ0NL/BqTlHETLX2K0yEuAbTkA0cJIjBYDiWdAv7QlA7UcKzjYcy6dR+IGIVv6k489/Nlml9gvcXjjgx9l1nL8MwjZcgDGkjgAjkwe/OpBtjTAWPwNuFWNglEwCkbByAYAkXFFw+ttf5kAAAAASUVORK5CYII=","orcid":"","institution":"Heilongjiang University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Gao","suffix":""},{"id":373363790,"identity":"adb8b980-4fdb-4707-abb6-e7c82fe38c41","order_by":2,"name":"Linlin Li","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Li","suffix":""},{"id":373363791,"identity":"ef6df976-d7f1-4d4a-a819-ae93305e42eb","order_by":3,"name":"Yutong Li","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yutong","middleName":"","lastName":"Li","suffix":""},{"id":373363792,"identity":"9606e9ae-83c1-44c2-99b5-54e15c7d0655","order_by":4,"name":"Shutian Liu","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shutian","middleName":"","lastName":"Liu","suffix":""},{"id":373363793,"identity":"21a9a2d6-83d7-4163-9227-530b80f140ea","order_by":5,"name":"Zhengjun Liu","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhengjun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-10-22 11:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5311257/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5311257/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87636351,"identity":"e53d7d32-706a-4660-9153-fa79127563ea","added_by":"auto","created_at":"2025-07-26 12:01:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":446179,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript1022.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5311257/v1_covered_2f1db646-cba0-4bf0-befd-d6031012fc2e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Small-target traffic sign detection based on improved YOLOv8","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":"YOLOv8, Small target detection, BRA, CARAFE, Complex road scenes","lastPublishedDoi":"10.21203/rs.3.rs-5311257/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5311257/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To address the challenges of high proportions of small traffic signs and significant environmental interference in road traffic scenarios, an improved YOLOv8 network specifically designed for small traffic sign detection is proposed. 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