Personal Protective Equipment Detection Method Based on Knowledge Distillation

Personal Protective Equipment Detection Method Based on Knowledge Distillation | 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 Personal Protective Equipment Detection Method Based on Knowledge Distillation Chonghua Zhou, Ruixuan Zhang, Yixi Fu, Xiaofei Wen, Shuangshuang Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8054766/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose: Automatic detection of Personal Protective Equipment (PPE) is critical for ensuring safety in construction sites. However, current methods struggle with two major challenges. One is the limited diversity of training data, which restricts robustness in adverse environments. The other is the accuracy–efficiency trade-off inherent in lightweight models. Methods: We propose a detection framework called Distillation Alignment YOLO (DA-YOLO) for PPE detection. Our method enhances data diversity through controllable augmentation of adverse conditions and introduces a teacher–student distillation framework with consistency constraints across predictions and features. This joint constraint enables the student model to achieve strong generalization while maintaining low computational cost. Results: Experimental results on public PPE dataset show that DA-YOLO achieves the best performance under the same parameter size with mAP50 reaching 92.81%, representing an improvement of 5.5% over YOLOv13. Conclusion: By integrating controllable augmentation and a dual-consistency distillation strategy, our method mitigates the critical issues in smart construction site. Smart Construction Site Personal Protective Equipment Detection Knowledge Distillation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviews received at journal 31 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 19 Jan, 2026 Editor assigned by journal 19 Dec, 2025 Submission checks completed at journal 19 Nov, 2025 First submitted to journal 07 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. 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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-8054766","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576788873,"identity":"16634d5d-8d74-4e2b-8a9c-39eacd9fb647","order_by":0,"name":"Chonghua Zhou","email":"","orcid":"","institution":"University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Chonghua","middleName":"","lastName":"Zhou","suffix":""},{"id":576788874,"identity":"635e273f-e8be-40d2-83bd-f73ab87bab24","order_by":1,"name":"Ruixuan 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Distillation","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":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Smart Construction Site, Personal Protective Equipment, Detection, Knowledge Distillation","lastPublishedDoi":"10.21203/rs.3.rs-8054766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8054766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose:\u003c/h2\u003e\u003cp\u003eAutomatic detection of Personal Protective Equipment (PPE) is critical for ensuring safety in construction sites. However, current methods struggle with two major challenges. One is the limited diversity of training data, which restricts robustness in adverse environments. The other is the accuracy\u0026ndash;efficiency trade-off inherent in lightweight models.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe propose a detection framework called Distillation Alignment YOLO (DA-YOLO) for PPE detection. Our method enhances data diversity through controllable augmentation of adverse conditions and introduces a teacher\u0026ndash;student distillation framework with consistency constraints across predictions and features. This joint constraint enables the student model to achieve strong generalization while maintaining low computational cost.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eExperimental results on public PPE dataset show that DA-YOLO achieves the best performance under the same parameter size with mAP50 reaching 92.81%, representing an improvement of 5.5% over YOLOv13.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eBy integrating controllable augmentation and a dual-consistency distillation strategy, our method mitigates the critical issues in smart construction site.\u003c/p\u003e","manuscriptTitle":"Personal Protective Equipment Detection Method Based on Knowledge Distillation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 18:07:40","doi":"10.21203/rs.3.rs-8054766/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-05T09:58:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T14:55:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68695362339193700911121014195011463323","date":"2026-02-26T02:29:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311268180424449160057070638044751783525","date":"2026-02-25T15:54:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-01T02:53:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311791038615805790894090069991144119863","date":"2026-01-21T13:58:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-19T11:11:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-19T09:04:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-19T14:06:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Multimedia Systems","date":"2025-11-07T08:13:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"688108cf-f336-4412-9751-758a714eb9ed","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-03T12:10:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 18:07:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8054766","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8054766","identity":"rs-8054766","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}