Research on belt deviation diagnosis of belt conveyors based on deep learning

Research on belt deviation diagnosis of belt conveyors based on deep learning | 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 Research on belt deviation diagnosis of belt conveyors based on deep learning Lei Wu, Yahu Wang, Wei Zhang, Shuai Huang, Junxia Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4608494/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 Due to the slow detection speed, low accuracy, and small detection range of existing methods for detecting belt deviation in belt conveyors, this paper introduces an enhanced Ultra-Fast Lane Detection (UFLD) algorithm that leverages deep learning for the detection of belt deviation. Based on the UFLD algorithm, a variable step-size row anchor division method is proposed, and the Convolutional Block Attention Module (CBAM) is added to the network to enhance the feature extraction capabilities. Furthermore, improvements are made to the convolution operations in the ResNet-18 Stem and the downsampling operations in the residual modules, thereby enhancing the network's ability to detect the edges of conveyor belts. Based on the established experimental platform, a high-definition camera equipped with a track-type inspection robot was used to inspect the entire belt conveyor, covering the whole of the transmission line. The conveyor belt operation datasets collected under various working conditions were used to train and comparatively study the DHT, YOLOv5, LaneNet, SAD, and UFLD algorithms. The experimental outcomes demonstrate that the algorithm introduced in this article outperforms the other algorithms, achieving an F1-measure of 90.41%, an accuracy rate of 94.27%, and a detection speed of 39 frames per second (FPS), meeting the real-time diagnostic needs for belt misalignment in the coal mining industry. conveyor belt deviation deep learning UFLD CBAM inspection robot 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-4608494","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":316899794,"identity":"3c890204-d520-4f35-8114-4a8caf33837a","order_by":0,"name":"Lei Wu","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wu","suffix":""},{"id":316899796,"identity":"6181a2c9-eaa0-4c90-8b9d-7ac991687a8f","order_by":1,"name":"Yahu Wang","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yahu","middleName":"","lastName":"Wang","suffix":""},{"id":316899797,"identity":"06366626-8210-4ddd-a5c1-6be506191d51","order_by":2,"name":"Wei Zhang","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""},{"id":316899798,"identity":"a53ffad2-9f0e-4a76-86ea-72de274e6540","order_by":3,"name":"Shuai Huang","email":"","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Huang","suffix":""},{"id":316899799,"identity":"6127f381-1418-41e1-8dd0-ff7173676b70","order_by":4,"name":"Junxia Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIie2PMQrCMBiF/xBwimRtwENkC4GiVykUnDxAx0ChZ4gXcf5DVzGr0Em8QHoDE0/wdxPMt7zlffAeQKXyg2gAhg20QkpHVjhk5XxQHrcoAHOrXUdUjIyIdopCA7K0XgiK9X0edl+E4Y6r640y7Fm+DIuwDnd8T1LinBX9EBo7qoJl2IAblPxF5y+9UD6MtC9GhtdbTceTlGNIK0Up8OYbzBH7pZvo3UqlUvlHPjYqNlRGTo+IAAAAAElFTkSuQmCC","orcid":"","institution":"Taiyuan University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Junxia","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-06-20 01:59:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4608494/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4608494/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61600325,"identity":"f04ca754-d090-479c-b66f-77339fa4912e","added_by":"auto","created_at":"2024-08-01 17:36:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1208255,"visible":true,"origin":"","legend":"","description":"","filename":"Researchonbeltdeviationdiagnosisofbeltconveyorsbasedondeeplearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4608494/v1_covered_0c4a2035-6136-49de-a452-af5d5da21557.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on belt deviation diagnosis of belt conveyors based on deep learning","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":"conveyor belt deviation, deep learning, UFLD, CBAM, inspection robot","lastPublishedDoi":"10.21203/rs.3.rs-4608494/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4608494/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to the slow detection speed, low accuracy, and small detection range of existing methods for detecting belt deviation in belt conveyors, this paper introduces an enhanced Ultra-Fast Lane Detection (UFLD) algorithm that leverages deep learning for the detection of belt deviation. Based on the UFLD algorithm, a variable step-size row anchor division method is proposed, and the Convolutional Block Attention Module (CBAM) is added to the network to enhance the feature extraction capabilities. Furthermore, improvements are made to the convolution operations in the ResNet-18 Stem and the downsampling operations in the residual modules, thereby enhancing the network's ability to detect the edges of conveyor belts. Based on the established experimental platform, a high-definition camera equipped with a track-type inspection robot was used to inspect the entire belt conveyor, covering the whole of the transmission line. The conveyor belt operation datasets collected under various working conditions were used to train and comparatively study the DHT, YOLOv5, LaneNet, SAD, and UFLD algorithms. 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