DRR-YOLO: A Multiscale Wood Surface Defect Detection Method Based on Improved YOLOv8

DRR-YOLO: A Multiscale Wood Surface Defect Detection Method 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 DRR-YOLO: A Multiscale Wood Surface Defect Detection Method Based on Improved YOLOv8 Rijun WANG, Yesheng CHEN, Guanghao ZHANG, Fulong LIANG, Bo WANG, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4931405/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 Wood surface defect detection technology offers the advantages of being non-destructive, rapid, accurate, and economical. It plays a crucial role in wood grade sorting, defect detection, improving the quality of sawn timber, and accelerating the automation of wood processing. Currently, there are challenges in accurately identifying multi-scale wood defects and insufficient overall detection accuracy in the field of wood defect detection. To address these issues, a new wood defect detection model named DRR-YOLO is proposed in this study. This proposed model combines the DWR module and the DRB module to innovatively form the DRRB module, replacing the bottleneck part of the C2f module in the YOLOv8 backbone, thereby constructing the C2f-DRRB module. This module effectively extracts multi-scale feature information. Additionally, by introducing the LSKA attention mechanism, the DRR-YOLO captures a wider range of global information. The neck structure of the DRR-YOLO is reconstructed using BiFPN, further enhancing the integration of feature information. In a series of ablation and comparative experiments, the DRR-YOLO model demonstrates superior performance, with its mean average precision (mAP) improved by 5.2% compared to the original algorithm. This effectively meets the wood industry's demand for accurate detection of wood defects. deep learning C2f-DRRB module wood defect detection LSKA attention mechanism 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-4931405","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346882905,"identity":"80306015-dd9d-4195-9301-ce76eab81987","order_by":0,"name":"Rijun WANG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYNACAzYGBmbmgw8+VEjI8ROvhZ0t2XDGGQtjyQaibeLnMZPmbatI3EBIi8Hxs4dfVxTwJW5n5jE24J0nwbiBgfnhoxv4tJzJS7M8Y8CWuLOZrfCB5DYJZnMGNmPjHDxazA7kmBk2ALVsOMy82cBwmwSbZQMPmzReLeffwLQwmEkkzpHgMThASMuNHOOHEC0sZhIHGyQkCGqxv/HGjBGoxXjDYWAgNxyTMJBsJuAXyf4c448Nf47Jbjh/+ODjPzV19f3szQ8f49MCBGwSDAzHkPjM+JWDlXxgYKghrGwUjIJRMApGLgAA4m1MX4Tg8fQAAAAASUVORK5CYII=","orcid":"","institution":"Guangxi Normal University","correspondingAuthor":true,"prefix":"","firstName":"Rijun","middleName":"","lastName":"WANG","suffix":""},{"id":346882906,"identity":"14a97515-ace4-4a89-b637-83b1dacdf9ef","order_by":1,"name":"Yesheng CHEN","email":"","orcid":"","institution":"Guangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yesheng","middleName":"","lastName":"CHEN","suffix":""},{"id":346882907,"identity":"f7030c82-0b0e-4ce9-b04e-577578461c09","order_by":2,"name":"Guanghao ZHANG","email":"","orcid":"","institution":"Guangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Guanghao","middleName":"","lastName":"ZHANG","suffix":""},{"id":346882908,"identity":"ea5522a9-c6b1-4883-b97a-6771deefdf87","order_by":3,"name":"Fulong LIANG","email":"","orcid":"","institution":"Guangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Fulong","middleName":"","lastName":"LIANG","suffix":""},{"id":346882909,"identity":"0bb4c40c-843c-4950-b0f2-ef9622199108","order_by":4,"name":"Bo WANG","email":"","orcid":"","institution":"Hechi University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"WANG","suffix":""},{"id":346882912,"identity":"172ba95b-3976-4922-8d57-767961d6c61f","order_by":5,"name":"Xiangwei MOU","email":"","orcid":"","institution":"Guangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiangwei","middleName":"","lastName":"MOU","suffix":""}],"badges":[],"createdAt":"2024-08-18 01:08:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4931405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4931405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65455239,"identity":"05aeaf0e-f186-4de4-a9b5-25582f861899","added_by":"auto","created_at":"2024-09-27 16:02:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1671461,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4931405/v1_covered_05c087d0-e8c4-422a-b7c3-425010f3398e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DRR-YOLO: A Multiscale Wood Surface Defect Detection Method 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":"deep learning, C2f-DRRB module, wood defect detection, LSKA attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-4931405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4931405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWood surface defect detection technology offers the advantages of being non-destructive, rapid, accurate, and economical. 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