Lightweight Road Traffic Sign Detection Based on Ghost-Net

Lightweight Road Traffic Sign Detection Based on Ghost-Net | 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 Lightweight Road Traffic Sign Detection Based on Ghost-Net Zenghui Wang, Xiaoquan Yue, Yuhang Wang, Zhiwu Feng, Jun Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6851491/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 The autonomous driving system relies heavily on the detection of traffic signs. In this paper, we propose an enhanced lightweight model based on YOLOv8 to address the issues of excessive model parameters, high computational cost, and inadequate small-target detection performance in traffic sign identification tasks. The model parameters and computational expenses are greatly decreased by substituting traditional convolutional layers with GhostNet modules in the back-bone network. The redundant P5 large-object detection head is eliminated to maximize com-putational efficiency, and a specialized P2 small-object detection layer is added to improve high-resolution feature extraction capabilities. Additionally, the feature fusion step incorporates a parameter-free SimAM attention method to dynamically improve feature weight allocation, increasing the accuracy of detection. The enhanced model achieves a higher mean Average Precision ( [email protected] ) of 89.1% compared to the baseline model's 87.6%, while reducing pa-rameters from 11.1M to 3.6M (67.57% reduction) and computational costs from 28.5 GFLOPs to 20.9 GFLOPs (26.67% reduction), according to experimental results on the TT100K dataset. Lightweight SimAM GhostNet YOLOv8 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. 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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-6851491","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470095441,"identity":"0169bd0e-55cf-4302-a450-119f29493896","order_by":0,"name":"Zenghui Wang","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Zenghui","middleName":"","lastName":"Wang","suffix":""},{"id":470095442,"identity":"d79f0eae-f212-45d8-9bd9-78bd7e7b713e","order_by":1,"name":"Xiaoquan Yue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYLACxgYbfjAjoYB4LWmSDWAtBsRrOQzRwkCMFoMbyZs/V+44L8EvkZ344YEBgzy/2AECWs4cKzA8e+a2hGTP2c0SQIcZzpydQEDL8R6DxMa223UGx3s3gLQkGNwmpOUwj8HBxrZzEgaHeTf/IE7L8R7Dxsa2AxJAW7YRZ4vkmWPFjI1tySC/bLNIMJAg7Bc+YIh9bGyzA4ZY7uabPyps5PmlCWhROIAaFxL4lYOAfANR0TcKRsEoGAUjGgAAe3xGPlCN2UAAAAAASUVORK5CYII=","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoquan","middleName":"","lastName":"Yue","suffix":""},{"id":470095443,"identity":"08d5ab08-c6f4-4aea-bf4b-d61f441cbe20","order_by":2,"name":"Yuhang Wang","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Yuhang","middleName":"","lastName":"Wang","suffix":""},{"id":470095447,"identity":"5ba477f2-020b-4604-b890-09bc71186f1c","order_by":3,"name":"Zhiwu Feng","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwu","middleName":"","lastName":"Feng","suffix":""},{"id":470095448,"identity":"31235546-aef6-4239-b0bc-4acd98a4ff12","order_by":4,"name":"Jun Yu","email":"","orcid":"","institution":"Fujian Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-06-09 06:53:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6851491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6851491/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86614626,"identity":"b143d256-67ec-4f96-b1fa-5e0c91fe1e55","added_by":"auto","created_at":"2025-07-13 23:16:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":722706,"visible":true,"origin":"","legend":"","description":"","filename":"LightweightRoadTrafficSignDetectionBasedonGhost.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6851491/v1_covered_12ad9f03-f72a-401d-a56e-83cd13fa01ac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lightweight Road Traffic Sign Detection Based on Ghost-Net","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Lightweight, SimAM, GhostNet, YOLOv8","lastPublishedDoi":"10.21203/rs.3.rs-6851491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6851491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe autonomous driving system relies heavily on the detection of traffic signs. In this paper, we propose an enhanced lightweight model based on YOLOv8 to address the issues of excessive model parameters, high computational cost, and inadequate small-target detection performance in traffic sign identification tasks. The model parameters and computational expenses are greatly decreased by substituting traditional convolutional layers with GhostNet modules in the back-bone network. The redundant P5 large-object detection head is eliminated to maximize com-putational efficiency, and a specialized P2 small-object detection layer is added to improve high-resolution feature extraction capabilities. Additionally, the feature fusion step incorporates a parameter-free SimAM attention method to dynamically improve feature weight allocation, increasing the accuracy of detection. 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