Small Traffic Sign Recognition Method Based on Improved YOLOv7

Small Traffic Sign Recognition Method Based on Improved YOLOv7 | 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Article Small Traffic Sign Recognition Method Based on Improved YOLOv7 Bo Meng, Weida Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5050877/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract As autonomous and assisted driving technologies progress rapidly, the significance of traffic sign recognition intensifies. Currently, the detection accuracy of algorithms for traffic sign recognition remains suboptimal, particularly when identifying small traffic signs amid complex backgrounds and under inadequate lighting, leading frequently to errors in detection. This paper introduces an enhanced method for small traffic sign recognition, underpinned by an improved version of YOLOv7. Initially, The Spatial Pyramid Pooling Fast and Cross-Stage Partial Connection (SPPFCSPC) strategy was used to improve the feature extraction of small targets. Subsequently, a ShuffleAttention-CARAFE (S-CARAFE) upsampling operator is crafted. S-CARAFE refocuses on key features within the input data, boosting the information detail and improving feature recombination. Finally, the introduction of a new Normalized Wasserstein Distance (NWD) method resolves the traditional IoU measurement's sensitivity to small-target traffic signs. Experimental results show that the [email protected] and [email protected] :0.9 values of the model trained on the TT100K dataset are increased by 3.48% and 2.29%, respectively. Additionally, the algorithm's improvements are validated on the small-target characteristics of the CCTSDB dataset and the sorted foreign traffic sign dataset, effectively elevating the recognition of small traffic signs across varying environments, consequently advancing the traffic sign recognition capacity of autonomous driving systems. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Information technology traffic sign recognition SPPFCSPC S-CARAFE NWD Small target Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 Nov, 2024 Reviews received at journal 05 Nov, 2024 Reviews received at journal 05 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviews received at journal 29 Oct, 2024 Reviewers agreed at journal 26 Oct, 2024 Reviewers agreed at journal 24 Oct, 2024 Reviewers agreed at journal 24 Oct, 2024 Reviewers agreed at journal 24 Oct, 2024 Reviewers agreed at journal 24 Oct, 2024 Reviewers invited by journal 24 Oct, 2024 Editor assigned by journal 24 Oct, 2024 Editor invited by journal 17 Sep, 2024 Submission checks completed at journal 16 Sep, 2024 First submitted to journal 07 Sep, 2024 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. 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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-5050877","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":374879686,"identity":"51961701-c6b8-445b-a2cb-770f79e98bf1","order_by":0,"name":"Bo Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYJACZgSzQkJOnkQtZyyMDRtI0sLYVpHIcICAct323sOvCyoY8vhn9x78XDhPIoGxgfnhoxt4tJidOZdmPeMMQ7HEnXPJ0jO3SeSxM7AZG+fg03Ijx8yYt40hseFGjoE07zaJYsYGHjZpvFruv4FomX8jx/g37xyJxIYDhLTc4DF+DNKyAWidNG8DMVrO5JgxA/2SuBGoxZrnmISxYTMhvxw/Y/wZGGKJ84AOu81TUycnz9788DE+LUDAJsHA8B+Jz4xTJULJB8JqRsEoGAWjYEQDAFOvSKQ+gSV9AAAAAElFTkSuQmCC","orcid":"","institution":"Northeast Electric Power University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Meng","suffix":""},{"id":374879688,"identity":"5913e9cd-1e58-4078-b9cd-3c84d57e474a","order_by":1,"name":"Weida Shi","email":"","orcid":"","institution":"Northeast Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Weida","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2024-09-08 03:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5050877/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5050877/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-88679-w","type":"published","date":"2025-02-14T15:57:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76487731,"identity":"72783443-b031-40a0-becd-6e130b2877e5","added_by":"auto","created_at":"2025-02-17 16:11:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1049514,"visible":true,"origin":"","legend":"","description":"","filename":"4.SmallTrafficSignRecognitionMethodBasedonImprovedYOLOv7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5050877/v1_covered_027ba15e-8f17-4247-a7bc-f05113549b70.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Small Traffic Sign Recognition Method Based on Improved YOLOv7","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"traffic sign recognition, SPPFCSPC, S-CARAFE, NWD, Small target","lastPublishedDoi":"10.21203/rs.3.rs-5050877/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5050877/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs autonomous and assisted driving technologies progress rapidly, the significance of traffic sign recognition intensifies. 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