A Novel Deep Learning Model Based on YOLOv5 Optimal Method for Coal Gangue Image Recognition

A Novel Deep Learning Model Based on YOLOv5 Optimal Method for Coal Gangue Image Recognition | 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 A Novel Deep Learning Model Based on YOLOv5 Optimal Method for Coal Gangue Image Recognition Tongkai Gu, Haiyan Zhao, Yasheng Chang, Sitong Yan, Feihan Cao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5046569/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 May, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Coal gangue recognition presents significant challenges in the mining industry due to its inefficient and costly traditional treatment methods. The advent of deep learning techniques has introduced novel solutions for automating and online coal gangue processing. Despite the potential of deep learning models, challenges such as overfitting and the need for extensive labeled datasets hinder their effectiveness. You Only Look Once version 5 (YOLOv5), with its rapid inference speed and high accuracy, offers a suitable solution for real-time coal gangue detection. This research investigates the application of YOLOv5 for coal gangue image recognition, involving data preprocessing, model training, and optimization. Experimental results demonstrate that incorporating the multiple channel attention (MCA) mechanism and lightweight content-aware re-assembly of features (CARAFE) up-sampling operator significantly improves model confidence and recognition performance. Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Information technology Coal gangue detection YOLOv5 Overfitting Preprocessing Detection accuracy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 05 May, 2025 Reviews received at journal 03 May, 2025 Reviews received at journal 25 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviewers invited by journal 21 Apr, 2025 Submission checks completed at journal 20 Apr, 2025 First submitted to journal 16 Apr, 2025 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-5046569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":445821714,"identity":"bec6f591-7aad-48cb-a487-cc9bc58969b4","order_by":0,"name":"Tongkai Gu","email":"","orcid":"","institution":"Xi’an University of Architecture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tongkai","middleName":"","lastName":"Gu","suffix":""},{"id":445821715,"identity":"d1197334-cd09-42b2-8e7a-f115b41f437f","order_by":1,"name":"Haiyan Zhao","email":"","orcid":"","institution":"kunshan dengyun college of science and technology","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Zhao","suffix":""},{"id":445821717,"identity":"870dafe4-e7ba-4538-8afd-fd77259cc3bc","order_by":2,"name":"Yasheng Chang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnklEQVRIiWNgGAWjYNCCCgk5fhK1nLEwlmwgSQdjW0XiBqK18Lc3P5P8OU+CcQMD88NHN4jRInHmmJk07zYJZnMGNmPjHGK0GEjksEkzbpNgs2zgYZMmWovkzzkSPAYHSNEiwdsgIUG8FqBfjK15jkkYSDYT6xdgiD28+aOmrr6fvfnhY6K0AAGLBJhiJlI5WO0HEhSPglEwCkbBSAQAggsoipDPS6UAAAAASUVORK5CYII=","orcid":"","institution":"Suzhou City University","correspondingAuthor":true,"prefix":"","firstName":"Yasheng","middleName":"","lastName":"Chang","suffix":""},{"id":445821719,"identity":"7acf0348-0fc5-4b8c-8d0d-2a19c844ff72","order_by":3,"name":"Sitong Yan","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Sitong","middleName":"","lastName":"Yan","suffix":""},{"id":445821721,"identity":"b3239551-1f7f-4ec1-96a7-5d700260d306","order_by":4,"name":"Feihan Cao","email":"","orcid":"","institution":"Suzhou City University","correspondingAuthor":false,"prefix":"","firstName":"Feihan","middleName":"","lastName":"Cao","suffix":""},{"id":445821722,"identity":"c323811d-c2fc-4f6f-91e8-6893f6d2d0f1","order_by":5,"name":"Wei Liu","email":"","orcid":"","institution":"Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-09-07 01:30:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5046569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5046569/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-01312-8","type":"published","date":"2025-05-28T15:56:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83782702,"identity":"dcec938f-a17a-4458-b415-2b5498968377","added_by":"auto","created_at":"2025-06-02 15:59:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":774344,"visible":true,"origin":"","legend":"","description":"","filename":"withoutredRevisionANovelDeepLearningModelBasedonYOLOv5OptimalMethodforCoalGangueImageRecognition.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5046569/v1_covered_e377b113-b0ea-49ac-859e-c4e3f9e3a92c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Deep Learning Model Based on YOLOv5 Optimal Method for Coal Gangue Image Recognition","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coal gangue detection, YOLOv5, Overfitting, Preprocessing, Detection accuracy","lastPublishedDoi":"10.21203/rs.3.rs-5046569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5046569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoal gangue recognition presents significant challenges in the mining industry due to its inefficient and costly traditional treatment methods. 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