YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model

YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model | 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 YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model Jiayi zeng, Han Zhong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4199735/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale cracks and high costs in road damage detection, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (Pavement Disease). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the Large Separable Kernel Attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road diseases while reducing the computational load. Furthermore, we introduced Lightweight Shared Convolution Detection Head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. Finally, extensive experiments on the RDD2022 dataset yield a model with parametric and computational quantities of 2.3M and 6.1 GFLOPs, which are only 74.1% and 74.3% of the baseline, and the mAP reaches an improvement of 1.4 percentage points from the baseline. In addition, experimental results on the RoadDamage dataset show that the mAP increased by 4.2% and this algorithm has good robustness. This method can provide a reference for the automatic detection method of pavement cracks. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 May, 2024 Reviews received at journal 30 Apr, 2024 Reviews received at journal 25 Apr, 2024 Reviews received at journal 24 Apr, 2024 Reviews received at journal 20 Apr, 2024 Reviews received at journal 19 Apr, 2024 Reviewers agreed at journal 16 Apr, 2024 Reviewers agreed at journal 15 Apr, 2024 Reviewers agreed at journal 15 Apr, 2024 Reviewers agreed at journal 15 Apr, 2024 Reviewers agreed at journal 15 Apr, 2024 Reviewers invited by journal 15 Apr, 2024 Editor assigned by journal 15 Apr, 2024 Editor invited by journal 08 Apr, 2024 Submission checks completed at journal 08 Apr, 2024 First submitted to journal 01 Apr, 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-4199735","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":289491529,"identity":"cf163112-ffae-4c01-a083-e09269b54282","order_by":0,"name":"Jiayi zeng","email":"","orcid":"","institution":"China People's Public Security University","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"zeng","suffix":""},{"id":289491532,"identity":"d5256ca6-8b68-4b8e-822c-35e11d20ba01","order_by":1,"name":"Han Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIie3QIQ7CMBTG8TZNinlMl4zQK7xlCQrBUYrZ1BLkJJjW7AAThGOgu5CgdgMM84jJCRIYCrknEf3p9xffYywI/hIw3+NmJWfOkxPe1fssjaA15ESk0F93Z7VFWqAP81sMKHKrmGFDeZlO+CHKFieUhY2PnlftfToRDNbqiVDYpTeCW0IivwmgyqUySEuAQZoAoqEnSkDS1WgSOz65IW3RrkLfv95aO9c8hpKQjPt/POE+CIIgoPgAIwk0oHh6ptMAAAAASUVORK5CYII=","orcid":"","institution":"China People's Public Security University","correspondingAuthor":true,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhong","suffix":""}],"badges":[],"createdAt":"2024-04-01 09:42:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4199735/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4199735/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54523177,"identity":"99010a52-a292-4f81-b679-c2d8f69c0c79","added_by":"auto","created_at":"2024-04-11 18:48:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2004421,"visible":true,"origin":"","legend":"","description":"","filename":"YOLOv8PDAnImprovedRoaddamagedetectionalgorithmbasedonYOLOv8nmodel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4199735/v1_covered_7a51ff4d-de88-4e60-bc7e-b528434052fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"YOLOv8-PD:An Improved Road damage detection algorithm based on YOLOv8n model ","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4199735/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4199735/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Road damage detection is an crucial task to ensure road safety. To tackle the issues of poor performance on multi-scale cracks and high costs in road damage detection, this paper presents an improved lightweight road damage detection algorithm based on YOLOv8n, named YOLOv8-PD (Pavement Disease). Firstly, a BOT module that can extract global information of road damage images is proposed to adapt to the large-span features of crack objects. Secondly, the introduction of the Large Separable Kernel Attention (LKSA) mechanism enhances the detection accuracy of the algorithm. Then, a C2fGhost block is constructed in the neck network to strengthen the feature extraction of complex road diseases while reducing the computational load. Furthermore, we introduced Lightweight Shared Convolution Detection Head (LSCD-Head) to improve feature expressiveness and reduce the number of parameters. 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