An improved YOLOv8 safety helmet wearing detection network

An improved YOLOv8 safety helmet wearing detection network | 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 An improved YOLOv8 safety helmet wearing detection network Xudong Song, Tiankai Zhang, Weiguo Yi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3924274/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract In the field of industrial safety, wearing helmets plays a vital role in ensuring workers’ health. Aiming at addressing the complex background in the industrial environment, caused by differences in distance, the helmet small target wear detection methods for misdetection and omission detection problems are needed. An improved YOLOv8 safety helmet wearing detection network is proposed to enhance the capture of details, improve multiscale feature processing and improve the accuracy of small target detection by introducing Dilation-wise Residual(DWR) attention module, Atrous Spatial Pyramid Pooling(ASPP) and Normalized Wasserstein Distance(NWD) loss function. Experiments were conducted on the SHWD dataset, and the results showed that the mAP of the improved network improved to 92.0%, which exceeded that of the traditional target detection network in terms of accuracy, recall, and other key metrics. These findings further improved the detection of helmet wearing in complex environments and greatly enhanced the accuracy of detection. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology YOLOv8 attention mechanism pooled pyramid loss function safety helmet wearing detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Mar, 2024 Reviews received at journal 25 Feb, 2024 Reviewers agreed at journal 25 Feb, 2024 Reviewers agreed at journal 24 Feb, 2024 Reviewers invited by journal 24 Feb, 2024 Editor assigned by journal 18 Feb, 2024 Editor invited by journal 12 Feb, 2024 Submission checks completed at journal 11 Feb, 2024 First submitted to journal 03 Feb, 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-3924274","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":272389842,"identity":"119c3906-3f28-4c35-9e7a-ed16312d34f1","order_by":0,"name":"Xudong Song","email":"","orcid":"","institution":"Computer and communication engineering Institute of Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"Song","suffix":""},{"id":272389843,"identity":"0401d387-8348-4968-87ca-ba9cfdac8bf2","order_by":1,"name":"Tiankai Zhang","email":"","orcid":"","institution":"Computer and communication engineering Institute of Dalian Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Tiankai","middleName":"","lastName":"Zhang","suffix":""},{"id":272389844,"identity":"9a44ee6c-c394-439d-9f72-a305c2f65e0e","order_by":2,"name":"Weiguo Yi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYBACxmYGZjCDn735wIEPP0jRItlzLPHgzB7iLIJoMbiRY3yYg40Y9e3Mhw0+th3OMzhz5sNhBh4GeX6xA4QcxpacOOPM4WLJ470bDhdYMBjOnJ1ASAuP8WGeisOJfWfObjg8g4chweA2QS38nw//MTic2HAj58FhHjaitPAwJzMAbZlwI4eBWC1sxoY9Z9ITZ/YcMwAGsgRhvxj2H34s8bPNOrGfvfnxhw8/bOT5pQlpaUDlS+BXDgLyhJWMglEwCkbBiAcAmMNLZbGafLUAAAAASUVORK5CYII=","orcid":"","institution":"Computer and communication engineering Institute of Dalian Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Weiguo","middleName":"","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2024-02-03 14:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3924274/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3924274/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-68446-z","type":"published","date":"2024-07-30T15:57:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61793485,"identity":"75cd2702-60fe-4858-a8f1-22330d7b39f5","added_by":"auto","created_at":"2024-08-05 16:13:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":464935,"visible":true,"origin":"","legend":"","description":"","filename":"AnimprovedYOLOv8safetyhelmetwearingdetectionnetwork.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3924274/v1_covered_eda4f14d-4ebe-48e7-ae98-0e7874c1ab3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An improved YOLOv8 safety helmet wearing detection network","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":"YOLOv8, attention mechanism, pooled pyramid, loss function, safety helmet wearing detection","lastPublishedDoi":"10.21203/rs.3.rs-3924274/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3924274/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the field of industrial safety, wearing helmets plays a vital role in ensuring workers\u0026rsquo; health. 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