HLC-YOLOv8: An algorithm for disordered parts recognition based on improved YOLOv8

HLC-YOLOv8: An algorithm for disordered parts recognition based on improved YOLOv8 | 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 HLC-YOLOv8: An algorithm for disordered parts recognition based on improved YOLOv8 Jiazhong Xu, Xin Tong, Ge Song, Cheng Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4308552/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract In order to address the challenge of recognizing parts placed on an assembly line in a disordered manner, a disordered parts recognition algorithm HLC-YOLOv8 based on improved YOLOv8 is proposed. To enhance the accuracy and robustness of image recognition and processing, the HorNet module is introduced into the backbone network. This module is capable of effectively fusing features from different layers, thereby improving the feature extraction capability. Furthermore, to enhance computational efficiency and speed, the LightConv module is employed in the neck network. This module features a simpler structure with a smaller number of parameters, rendering it more efficient than the standard convolutional operation. In Addition, the ConTainer module is integrated into the conventional YOLOv8 architecture, which integrates and understands the contextual information in the image more efficiently, enhances the sensory field of the model, and improves the accuracy of small target recognition. The experimental results on the disordered parts datasets show that the improved model in this paper has better detection performance, and the detection accuracy and speed have been significantly improved to achieve the purpose of real-time identification of disordered parts. YOLOv8 HorNet LightConv ConTainer Disordered Parts Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Aug, 2024 Reviewers invited by journal 18 Aug, 2024 Editor assigned by journal 24 Apr, 2024 First submitted to journal 22 Apr, 2024 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-4308552","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341623938,"identity":"86887a67-9cae-47e4-89cc-573bed892ee5","order_by":0,"name":"Jiazhong Xu","email":"","orcid":"","institution":"Harbin University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jiazhong","middleName":"","lastName":"Xu","suffix":""},{"id":341623939,"identity":"45d57215-495c-4e1b-97bb-023eb2724d50","order_by":1,"name":"Xin Tong","email":"","orcid":"","institution":"Harbin University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Tong","suffix":""},{"id":341623940,"identity":"87e5b2fb-052d-4fd4-a8ac-6d9656a0267a","order_by":2,"name":"Ge Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYPACmwQGCQiLsYFILWkQLQdI0HKYBC3y7u0PHxf8Op9nPrvH+PMHBhvZDQeYnz3Ap8XwzBlj45l9t4tl7pwxkzjAkGa84QCbuQFeLTNy2KR5e24nzpDIMQM67HDihgM8bBJ4tcx//gyo5RxIi/GHAwz/CWuRl2Awk+b5cQCkxQDosAOEtRjw5Bgb8zYkJ86QOVYmccYg2XjmYTYz/La0H3/4mOePXeIM6ebNHyoq7GT7jjc/w2/LASDB2AbnAjEzPvUgWxpA5B8CqkbBKBgFo2BkAwDm10zbu1qWgQAAAABJRU5ErkJggg==","orcid":"","institution":"Harbin University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Ge","middleName":"","lastName":"Song","suffix":""},{"id":341623941,"identity":"09f375f7-fad8-46d4-83ff-dfeb9070f516","order_by":3,"name":"Cheng Huang","email":"","orcid":"","institution":"Harbin University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-04-23 01:22:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4308552/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4308552/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64488466,"identity":"f7097f60-3983-4e3d-9192-7d578e57944e","added_by":"auto","created_at":"2024-09-13 18:42:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1152712,"visible":true,"origin":"","legend":"","description":"","filename":"HLCYOLOv8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4308552/v1_covered_50185806-f30f-4ed7-8607-aeef15c28c0c.pdf"}],"financialInterests":"","formattedTitle":"HLC-YOLOv8: An algorithm for disordered parts recognition based on improved YOLOv8","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"YOLOv8, HorNet, LightConv, ConTainer, Disordered Parts","lastPublishedDoi":"10.21203/rs.3.rs-4308552/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4308552/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to address the challenge of recognizing parts placed on an assembly line in a disordered manner, a disordered parts recognition algorithm HLC-YOLOv8 based on improved YOLOv8 is proposed. To enhance the accuracy and robustness of image recognition and processing, the HorNet module is introduced into the backbone network. This module is capable of effectively fusing features from different layers, thereby improving the feature extraction capability. Furthermore, to enhance computational efficiency and speed, the LightConv module is employed in the neck network. This module features a simpler structure with a smaller number of parameters, rendering it more efficient than the standard convolutional operation. In Addition, the ConTainer module is integrated into the conventional YOLOv8 architecture, which integrates and understands the contextual information in the image more efficiently, enhances the sensory field of the model, and improves the accuracy of small target recognition. 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