Lightweight foreign object detection for mine conveyor belt based on improved YOLOv11n

Lightweight foreign object detection for mine conveyor belt based on improved YOLOv11n | 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 Lightweight foreign object detection for mine conveyor belt based on improved YOLOv11n Hai Rong, Zhouyong Xi, Longyue He, Mingyu Han, Shuai Deng, Zhenghao wang, and 1 more This is a preprint; it has not been peer reviewed by a journal.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-8395658/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 Addressing safety concerns such as conveyor belt damage and breakage caused by foreign objects during the operation of underground belt conveyors in coal mines, traditional detection methods suffer from high false-negative rates, poor real-time performance, or high costs. This paper proposes an improved foreign object detection algorithm for conveyor belts based on the lightweight object detection model YOLOv11n. It addresses the complex environmental challenges of uneven illumination, dust interference, and diverse foreign object shapes in mining shafts. In the backbone section, the C3k2_Faster_EMA module is introduced, enhancing the extraction of fine-grained foreign object features through heterogeneous convolutions and exponential moving average mechanisms. The Neck section embeds an SE attention module to dynamically amplify effective feature channels while suppressing background interference. A lightweight Detect_Efficient detection head employs separable convolutions to reduce computational overhead. Furthermore, the Inner-CIoU loss function optimizes bounding box regression accuracy for irregularly shaped foreign objects. The experimental results show that the improved model achieved P , R , [email protected] , and [email protected] –0.95 of 84.1%, 82.5%, 82.8%, and 80.2%, respectively, representing an improvement over the original model. Meanwhile, the number of parameters was reduced to 2.54 M, the FPS dropped to 102 frames, and the computational load was only 4.6 GFLOPs. Compared to mainstream models like YOLOv8n, the proposed model demonstrates superior performance in detection accuracy and lightweight efficiency. It achieves a balanced trade-off between detection precision and computational lightness, providing an effective technical solution for real-time intelligent safety monitoring of mine conveyor belts. Physical sciences/Engineering Physical sciences/Mathematics and computing foreign object detection mine conveyor belt YOLOv11n deep learning object detection lightweight 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-8395658","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":573694575,"identity":"2387f7a0-7da4-4223-997e-006f7154ace6","order_by":0,"name":"Hai Rong","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Rong","suffix":""},{"id":573694585,"identity":"9cb06790-46e3-4699-a24e-89a52c6c76a2","order_by":1,"name":"Zhouyong 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YOLOv11n","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":"foreign object detection, mine conveyor belt, YOLOv11n, deep learning, object detection, lightweight","lastPublishedDoi":"10.21203/rs.3.rs-8395658/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8395658/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAddressing safety concerns such as conveyor belt damage and breakage caused by foreign objects during the operation of underground belt conveyors in coal mines, traditional detection methods suffer from high false-negative rates, poor real-time performance, or high costs. This paper proposes an improved foreign object detection algorithm for conveyor belts based on the lightweight object detection model YOLOv11n. It addresses the complex environmental challenges of uneven illumination, dust interference, and diverse foreign object shapes in mining shafts. In the backbone section, the C3k2_Faster_EMA module is introduced, enhancing the extraction of fine-grained foreign object features through heterogeneous convolutions and exponential moving average mechanisms. The Neck section embeds an SE attention module to dynamically amplify effective feature channels while suppressing background interference. A lightweight Detect_Efficient detection head employs separable convolutions to reduce computational overhead. Furthermore, the Inner-CIoU loss function optimizes bounding box regression accuracy for irregularly shaped foreign objects. The experimental results show that the improved model achieved \u003cem\u003eP\u003c/em\u003e, \u003cem\u003eR\u003c/em\u003e, [email protected], and [email protected]\u0026ndash;0.95 of 84.1%, 82.5%, 82.8%, and 80.2%, respectively, representing an improvement over the original model. Meanwhile, the number of parameters was reduced to 2.54 M, the FPS dropped to 102 frames, and the computational load was only 4.6 GFLOPs. Compared to mainstream models like YOLOv8n, the proposed model demonstrates superior performance in detection accuracy and lightweight efficiency. It achieves a balanced trade-off between detection precision and computational lightness, providing an effective technical solution for real-time intelligent safety monitoring of mine conveyor belts.\u003c/p\u003e","manuscriptTitle":"Lightweight foreign object detection for mine conveyor belt based on improved YOLOv11n","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-14 17:53:34","doi":"10.21203/rs.3.rs-8395658/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"3d6687fe-9397-4332-8744-d378a8680796","owner":[],"postedDate":"January 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61040271,"name":"Physical sciences/Engineering"},{"id":61040272,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-02-27T09:55:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-14 17:53:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8395658","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8395658","identity":"rs-8395658","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}