Research on mine safety helmet detection algorithm based on multi-module collaborative optimization

Research on mine safety helmet detection algorithm based on multi-module collaborative optimization | 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 Research on mine safety helmet detection algorithm based on multi-module collaborative optimization Zhouyong Xi, Mingyu Han, Shuai Deng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8204852/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 To address the high false-negative rates, poor adaptability to complex environments, and deployment challenges associated with traditional manual inspections and existing YOLOv8 models in mine safety helmet detection, this study implements targeted improvements to the YOLOv8 algorithm and constructs an adapted model tailored to the specific characteristics of mine environments—such as low illumination and the prevalence of small-object helmets. Methodologically, the C2f-FE lightweight module replaces the original C2f module. It combines FasterNet Block with EMA attention mechanisms to balance small-object feature extraction and model lightweighting. The Dy-RepGFPN feature fusion network is introduced, utilizing dynamic upsampling and CSP_fusion modules to aggregate multi-scale features and suppress environmental interference. We innovated the UCDN-Head detection head, leveraging parameter sharing and independent BN layer calibration to optimize detection accuracy and environmental adaptability. Experiments conducted on a self-built dataset of 5,420 mining safety helmets yielded the following results: the improved model achieved [email protected] of 84.4%, an increase of 5.0% compared to the YOLOv8 baseline model; [email protected] was 81.5%, precision was 88.5%, and recall was 89.8%, with 3.1M parameters, 6.1 GFLOPs, and 80.5 FPS. The conclusions demonstrate that the improved model achieves comprehensive advantages of “high accuracy, lightweight, and high adaptability” for mine safety helmet detection, meeting real-time monitoring requirements in mines and providing technical support for head protection monitoring of underground personnel. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing mine safety helmet inspection YOLOv8 lightweight small object detection UCDN-Head Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 11 Dec, 2025 Editor assigned by journal 26 Nov, 2025 Submission checks completed at journal 26 Nov, 2025 First submitted to journal 25 Nov, 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. 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-8204852","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558605794,"identity":"292af7a0-b516-47dd-bd30-fd30c0af26aa","order_by":0,"name":"Zhouyong 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Methodologically, the C2f-FE lightweight module replaces the original C2f module. It combines FasterNet Block with EMA attention mechanisms to balance small-object feature extraction and model lightweighting. The Dy-RepGFPN feature fusion network is introduced, utilizing dynamic upsampling and CSP_fusion modules to aggregate multi-scale features and suppress environmental interference. We innovated the UCDN-Head detection head, leveraging parameter sharing and independent BN layer calibration to optimize detection accuracy and environmental adaptability. Experiments conducted on a self-built dataset of 5,420 mining safety helmets yielded the following results: the improved model achieved [email protected] of 84.4%, an increase of 5.0% compared to the YOLOv8 baseline model; [email protected] was 81.5%, precision was 88.5%, and recall was 89.8%, with 3.1M parameters, 6.1 GFLOPs, and 80.5 FPS. The conclusions demonstrate that the improved model achieves comprehensive advantages of “high accuracy, lightweight, and high adaptability” for mine safety helmet detection, meeting real-time monitoring requirements in mines and providing technical support for head protection monitoring of underground personnel.\u003c/p\u003e","manuscriptTitle":"Research on mine safety helmet detection algorithm based on multi-module collaborative optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-16 04:04:51","doi":"10.21203/rs.3.rs-8204852/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-12-11T06:02:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-26T07:11:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-26T07:07:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-25T15:06:12+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"3d6687fe-9397-4332-8744-d378a8680796","owner":[],"postedDate":"December 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59717807,"name":"Physical sciences/Engineering"},{"id":59717808,"name":"Earth and environmental sciences/Environmental sciences"},{"id":59717809,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-29T16:54:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-16 04:04:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8204852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8204852","identity":"rs-8204852","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}