Safety Equipment Wearing Detection Algorithm for Electric Power Workers Based on RepGFPN-YOLOv5

Safety Equipment Wearing Detection Algorithm for Electric Power Workers Based on RepGFPN-YOLOv5 | 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 Safety Equipment Wearing Detection Algorithm for Electric Power Workers Based on RepGFPN-YOLOv5 Yuanyuan Wang, Xiuchuan Chen, Yu Shen, Hauwa Suleiman Abdullahi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3844757/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 Wearing inspection safety equipment such as insulating gloves and safety helmets is an important guarantee for safe power operations. Given the low accuracy of the traditional insulating gloves and helmet-wearing detection algorithm and the problems of missed detection and false detection, this paper proposes an improved safety equipment wearing detection model named RepGFPN-YOLOv5 based on YOLOv5. This paper first uses the K-Means + + algorithm to analyze the data set for Anchor parameter size re-clustering to optimize the target anchor box size; secondly, it uses the neck network (Efficient Reparameterized Generalized Feature Pyramid Network, RepGFPN), which combines the efficient layer aggregation network ELAN and the re-parameterization mechanism), to reconstruct the YOLOv5 neck network to improve the feature fusion ability of the neck network; reintroduce the coordinate attention mechanism (Coordinate Attention, CA) to focus on small target feature information; finally, use WIoU_Loss as the loss function of the improved model to reduce prediction errors. Experimental results show that the RepGFPN-YOLOv5 model achieves an accuracy increase of 2.1% and an mAP value of 2.3% compared with the original YOLOv5 network, and detection speed of the improved model reaches 89FPS.The code: https://github.com/CVChenXC/RepGFPN-YOLOv5.git . insulating gloves safety helmet small target detection YOLO v5 RepGFPN attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Aug, 2024 Reviewers invited by journal 11 Jan, 2024 Submission checks completed at journal 09 Jan, 2024 Editor assigned by journal 09 Jan, 2024 First submitted to journal 08 Jan, 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. 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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-3844757","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266157335,"identity":"239e4fd5-1c91-4f70-b2d4-d0712a6c6234","order_by":0,"name":"Yuanyuan 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Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"insulating gloves, safety helmet, small target detection, YOLO v5, RepGFPN, attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-3844757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3844757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWearing inspection safety equipment such as insulating gloves and safety helmets is an important guarantee for safe power operations. Given the low accuracy of the traditional insulating gloves and helmet-wearing detection algorithm and the problems of missed detection and false detection, this paper proposes an improved safety equipment wearing detection model named RepGFPN-YOLOv5 based on YOLOv5. This paper first uses the K-Means\u0026thinsp;+\u0026thinsp;+\u0026thinsp;algorithm to analyze the data set for Anchor parameter size re-clustering to optimize the target anchor box size; secondly, it uses the neck network (Efficient Reparameterized Generalized Feature Pyramid Network, RepGFPN), which combines the efficient layer aggregation network ELAN and the re-parameterization mechanism), to reconstruct the YOLOv5 neck network to improve the feature fusion ability of the neck network; reintroduce the coordinate attention mechanism (Coordinate Attention, CA) to focus on small target feature information; finally, use WIoU_Loss as the loss function of the improved model to reduce prediction errors. Experimental results show that the RepGFPN-YOLOv5 model achieves an accuracy increase of 2.1% and an mAP value of 2.3% compared with the original YOLOv5 network, and detection speed of the improved model reaches 89FPS.The code: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/CVChenXC/RepGFPN-YOLOv5.git\u003c/span\u003e\u003cspan address=\"https://github.com/CVChenXC/RepGFPN-YOLOv5.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Safety Equipment Wearing Detection Algorithm for Electric Power Workers Based on RepGFPN-YOLOv5","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 04:12:43","doi":"10.21203/rs.3.rs-3844757/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-14T19:20:06+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-11T05:31:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-09T16:59:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-09T16:59:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Signal, Image and Video Processing","date":"2024-01-08T07:10:10+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"84062ca7-2f29-424e-ac5a-081f404d0716","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-04T11:23:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-11 04:12:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3844757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3844757","identity":"rs-3844757","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}