OSCD-YOLO: A Surface Crack Detection Method for Open-pit Mines in Complex Scenes

OSCD-YOLO: A Surface Crack Detection Method for Open-pit Mines in Complex Scenes | 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 OSCD-YOLO: A Surface Crack Detection Method for Open-pit Mines in Complex Scenes Xiaoyong Dong, Ran Ma, Weihui Tian, Keke Zhao, Zhaolin Zhong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7744083/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 This study proposes a crack detection model for complex scenes, named OSCD-YOLO, aimed at addressing the challenges of complex backgrounds and multi-scale features in surface crack detection in open-pit mining. The model integrates partial convolution to reduce redundant computations; multidimensional collaborative attention enhances the crack recognition ability in complex scenarios; and content-aware feature re-adjustment during upsampling improves the detection accuracy of small cracks. Experimental results on multiple datasets demonstrate that OSCD-YOLO outperforms existing models such as YOLOv8 and YOLO11 in both detection accuracy and inference speed. Particularly, on a custom-built dataset, OSCD-YOLO achieved an average precision improvement of 7.28%, reaching 91.7%. The experimental results indicate that OSCD-YOLO exhibits exceptional robustness, efficiency, and generalization capability in the complex open-pit mining environment, providing an efficient and real-time applicable crack detection solution. Open-pit mine surface cracks OSCD-YOLO Crack detection Generalization testing 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-7744083","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534105959,"identity":"4fd12901-7a48-4d1d-ab72-9b89f007a16a","order_by":0,"name":"Xiaoyong 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Scenes","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":"Open-pit mine surface cracks, OSCD-YOLO, Crack detection, Generalization testing","lastPublishedDoi":"10.21203/rs.3.rs-7744083/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7744083/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study proposes a crack detection model for complex scenes, named OSCD-YOLO, aimed at addressing the challenges of complex backgrounds and multi-scale features in surface crack detection in open-pit mining. The model integrates partial convolution to reduce redundant computations; multidimensional collaborative attention enhances the crack recognition ability in complex scenarios; and content-aware feature re-adjustment during upsampling improves the detection accuracy of small cracks. Experimental results on multiple datasets demonstrate that OSCD-YOLO outperforms existing models such as YOLOv8 and YOLO11 in both detection accuracy and inference speed. Particularly, on a custom-built dataset, OSCD-YOLO achieved an average precision improvement of 7.28%, reaching 91.7%. 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