ESW-YOLO: A lightweight YOLO model for defect detection in bottled liquor

ESW-YOLO: A lightweight YOLO model for defect detection in bottled liquor | 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 ESW-YOLO: A lightweight YOLO model for defect detection in bottled liquor Xuyang Wang, Xuerui Lan, Lijun Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5369988/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract With the growing adoption of deep learning in AI, flaw detection in bottled liquor production has become crucial to ensure product quality and consumer satisfaction. However, existing flaw detection models often face issues of low efficiency, particularly in multi-category and multi-target scenarios, and struggle with integration into resource-constrained devices. To solve these challenges, this study proposes ESW-YOLO, a lightweight model optimized to detect diverse flaws in bottled liquor production. This model is designed as follows: firstly, the Efficient Multi-Branch \& Scale FPN (EMBSFPN) is developed to reduce model size while increasing the detection accuracy of small flaws. Secondly, the SE attention mechanism is incorporated to emphasize critical features, which strengthens the model’s robustness in complex scenarios. Thirdly, the Wise-IoU loss function is used to optimize localization accuracy, particularly for irregular defects. Finally, a lightweight shared convolutional detection head (ESCD) is proposed to further decrease model size and improve detection efficiency. Experimental results on a bottled liquor flaw detection dataset demonstrate that ESW-YOLO achieves a mean average precision (mAP) of 94.7% and a recall of 91.8%. Additionally, the proposed model reduces computational cost by 30.8%, decreases parameter count by 45.1%, and maintains a compact model size of only 3.6 M. This method can provide a reference for the development of defect detection methods in bottled liquor. Defect detection EMBSFPN YOLOv8n Lightweight Wise-IoU Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Nov, 2024 Reviewers invited by journal 11 Nov, 2024 Editor assigned by journal 01 Nov, 2024 Submission checks completed at journal 01 Nov, 2024 First submitted to journal 31 Oct, 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. 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-5369988","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376429148,"identity":"e3c30fe8-5cd8-4062-a643-e7d6c22c586e","order_by":0,"name":"Xuyang Wang","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xuyang","middleName":"","lastName":"Wang","suffix":""},{"id":376429149,"identity":"56b37022-fa23-4880-8036-a2d205470b7b","order_by":1,"name":"Xuerui Lan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACNvaG9I8fftTI2bc3HyBOCx/PgWfMkj3HjA14jiUQp0VOIvEZAw8bc6KBRI4BkQ5jSE57IMHDlmDOkPPxxhsGOzndBoJajqUbFFjI5Fk2nN1sOYch2djsACEtjD0JEkBbihkO9m6T5mE4kLiNoBZm/g8SIL80HOZ5RqQWNoY0sJYNx3jYiNTCA3Q+KJAle9iMLecYEOEX+fkPEh+CopJf/vHDG28q7OQIakEBEjxERg2yFlJ1jIJRMApGwYgAAHq3PxyFlRO1AAAAAElFTkSuQmCC","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Xuerui","middleName":"","lastName":"Lan","suffix":""},{"id":376429150,"identity":"6ef08a5e-36a8-40ae-8f19-549f9b329a86","order_by":2,"name":"Lijun Liu","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-11-01 02:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5369988/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5369988/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68928127,"identity":"8d575c88-a0e0-4eea-8a61-0bfcafdb5001","added_by":"auto","created_at":"2024-11-13 14:56:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7299578,"visible":true,"origin":"","legend":"","description":"","filename":"ESWYOLO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5369988/v1_covered_146e786b-d4c7-417f-8dc0-2da4591329ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ESW-YOLO: A lightweight YOLO model for defect detection in bottled liquor","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":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Defect detection, EMBSFPN, YOLOv8n, Lightweight, Wise-IoU","lastPublishedDoi":"10.21203/rs.3.rs-5369988/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5369988/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the growing adoption of deep learning in AI, flaw detection in bottled liquor production has become crucial to ensure product quality and consumer satisfaction. However, existing flaw detection models often face issues of low efficiency, particularly in multi-category and multi-target scenarios, and struggle with integration into resource-constrained devices. To solve these challenges, this study proposes ESW-YOLO, a lightweight model optimized to detect diverse flaws in bottled liquor production. This model is designed as follows: firstly, the Efficient Multi-Branch \\\u0026amp; Scale FPN (EMBSFPN) is developed to reduce model size while increasing the detection accuracy of small flaws. Secondly, the SE attention mechanism is incorporated to emphasize critical features, which strengthens the model\u0026rsquo;s robustness in complex scenarios. Thirdly, the Wise-IoU loss function is used to optimize localization accuracy, particularly for irregular defects. Finally, a lightweight shared convolutional detection head (ESCD) is proposed to further decrease model size and improve detection efficiency. Experimental results on a bottled liquor flaw detection dataset demonstrate that ESW-YOLO achieves a mean average precision (mAP) of 94.7% and a recall of 91.8%. Additionally, the proposed model reduces computational cost by 30.8%, decreases parameter count by 45.1%, and maintains a compact model size of only 3.6 M. 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