Safety helmet wearing detection algorithm based on DSM-YOLO

Safety helmet wearing detection algorithm based on DSM-YOLO | 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 helmet wearing detection algorithm based on DSM-YOLO Jing Zhang, Yingying Feng, Xu Li, Ding Lang, Yuguang Xu, Hong-an Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3974560/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 Safety helmets can effectively prevent miners from accidental head injuries, reduce accident rates during coal mine, and safety helmet-wearing detection is of great significance to the safety management of coal mine, and is an important component of video surveillance systems. This paper proposes a new safety helmet-wearing detection algorithm called Depthwise Separable Multi-factor YOLO (DSM-YOLO). First, the algorithm uses Depthwise Separable Convolution(DSConv) to reduce the number of parameters, deepens the extraction of deep feature information, speed up feature transfer in the model, and improves the speed of helmet detection. Second, in order to make a better match between the predicted box of the target and the corresponding ground truth box, a multi-factor loss function is introduced, and the multi-factor loss function simultaneously takes into account the intersection ratio loss, distance loss, aspect ratio loss, and angle perception loss, which improves the accuracy of helmet detection. The results of the experiments showed that the inference speed of the algorithm is 62.5 Frames/s, which is 19.0 Frames/s faster than the YOLOv7 algorithm; the average precision is 95.1%, which is 3.4% higher than the YOLOv7 algorithm, meeting the real-time detection requirements for safety helmet wearing in coal mine. safety helmet wearing detection YOLOv7 algorithm depthwise separable convolution multi-factor loss 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 24 Feb, 2024 Submission checks completed at journal 21 Feb, 2024 Editor assigned by journal 21 Feb, 2024 First submitted to journal 20 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. 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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-3974560","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274043749,"identity":"da77af23-8976-4092-a452-c0ee81ccf86b","order_by":0,"name":"Jing Zhang","email":"","orcid":"","institution":"Xi’an University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":274043750,"identity":"1af60d2e-ce6c-41c1-82bd-c3b1bdefd765","order_by":1,"name":"Yingying 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