Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode

Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode | 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 Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode Xiangjun Du, Li Yu, Jun Wang, Dengjie Yang, Yao Zheng, Yimin Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4568035/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 paper presents a comprehensive study on enhancing the accuracy, real-time performance, and reliability of fault detection in conveyor belt drums. Leveraging insights from two distinct approaches, a novel lightweight network model, YOLOv8n + EMBC + SCC, is proposed. The model integrates the strengths of YOLOv8n in target detection accuracy and speed with innovative modules designed for improved performance. Firstly, the EMBC module, based on DSC high-efficiency convolution, replaces the traditional C2F module in the backbone and neck segments, resulting in a notable 14.5% increase in speed and a 0.7% enhancement in accuracy. Secondly, the SCC efficient convolution module replaces the Conv module in the detection head, further optimizing computational load and model performance, leading to an additional 11.73% increase in speed and a 0.7% improvement in accuracy. Experimental results demonstrate the efficacy of the proposed model, achieving a detection accuracy of 93.4%, surpassing YOLOv8n by 0.9%. Moreover, the model exhibits an improved Frames Per Second (FPS) value of 38.21, representing a 3.56 f/s advancement over YOLOv8n. Heatmap analysis validates the model's superiority in terms of high detection accuracy, precise fault identification, and clear fault localization. This research contributes to the development of a fast, precise, and reliable fault detection system suitable for conveyor belt drum applications, with implications for improving operational efficiency and maintenance practices in industrial settings. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Mechanical engineering Belt Conveyor Drum Image Processing Neural Network EMBC SCC 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. 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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-4568035","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":320706445,"identity":"3573d044-7769-41fd-be67-baeafa235422","order_by":0,"name":"Xiangjun Du","email":"data:image/png;base64,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","orcid":"","institution":"Tiangong University","correspondingAuthor":true,"prefix":"","firstName":"Xiangjun","middleName":"","lastName":"Du","suffix":""},{"id":320706448,"identity":"c065e475-205d-4244-929e-8db97fcb0a13","order_by":1,"name":"Li Yu","email":"","orcid":"","institution":"Tianjin Light Industry Vocational Technical College","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yu","suffix":""},{"id":320706449,"identity":"c964c8e5-01e8-469e-b54d-1c0c1c18dc92","order_by":2,"name":"Jun Wang","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wang","suffix":""},{"id":320706452,"identity":"3d4ac22d-d8a0-4dbf-94b6-1565d9f5697b","order_by":3,"name":"Dengjie Yang","email":"","orcid":"","institution":"Tiangong University","correspondingAuthor":false,"prefix":"","firstName":"Dengjie","middleName":"","lastName":"Yang","suffix":""},{"id":320706455,"identity":"263872b7-7af1-4c5e-b68b-1a35b8a1f12e","order_by":4,"name":"Yao Zheng","email":"","orcid":"","institution":"Tiangong University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Zheng","suffix":""},{"id":320706456,"identity":"2ea2fb58-94af-42b1-820f-30cfcfa2c4d6","order_by":5,"name":"Yimin Wang","email":"","orcid":"","institution":"Tiangong University","correspondingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-12 06:46:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4568035/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4568035/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79635973,"identity":"e97723f2-9e15-4b45-912d-e743486a161b","added_by":"auto","created_at":"2025-04-01 04:31:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1186168,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonConveyorBeltDrumFaultDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4568035/v1_covered_85b52a68-29d2-421f-89ed-8eb34784b84c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode","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":"Belt Conveyor, Drum, Image Processing, Neural Network, EMBC, SCC","lastPublishedDoi":"10.21203/rs.3.rs-4568035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4568035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents a comprehensive study on enhancing the accuracy, real-time performance, and reliability of fault detection in conveyor belt drums. Leveraging insights from two distinct approaches, a novel lightweight network model, YOLOv8n\u0026thinsp;+\u0026thinsp;EMBC\u0026thinsp;+\u0026thinsp;SCC, is proposed. The model integrates the strengths of YOLOv8n in target detection accuracy and speed with innovative modules designed for improved performance. Firstly, the EMBC module, based on DSC high-efficiency convolution, replaces the traditional C2F module in the backbone and neck segments, resulting in a notable 14.5% increase in speed and a 0.7% enhancement in accuracy. Secondly, the SCC efficient convolution module replaces the Conv module in the detection head, further optimizing computational load and model performance, leading to an additional 11.73% increase in speed and a 0.7% improvement in accuracy. Experimental results demonstrate the efficacy of the proposed model, achieving a detection accuracy of 93.4%, surpassing YOLOv8n by 0.9%. 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This research contributes to the development of a fast, precise, and reliable fault detection system suitable for conveyor belt drum applications, with implications for improving operational efficiency and maintenance practices in industrial settings.\u003c/p\u003e","manuscriptTitle":"Research on Fault Detection of Belt Conveyor Drum Based on Improved YOLOv8 Network Mode","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-01 02:40:25","doi":"10.21203/rs.3.rs-4568035/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":"4d850953-5d49-40b4-b2e7-2a35a0f988ea","owner":[],"postedDate":"July 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33906241,"name":"Physical sciences/Engineering/Electrical and electronic engineering"},{"id":33906242,"name":"Physical sciences/Engineering/Mechanical engineering"}],"tags":[],"updatedAt":"2025-04-01T04:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-01 02:40:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4568035","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4568035","identity":"rs-4568035","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}