Automated Face Mask Detection Using Dcnn and Mobilenet V3 for Covid19 Prevention

Automated Face Mask Detection Using Dcnn and Mobilenet V3 for Covid19 Prevention | 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 Automated Face Mask Detection Using Dcnn and Mobilenet V3 for Covid19 Prevention E Balraj, P Manikandan, M Sambath, J Omana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8266374/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 The rapid dissemination of the 2019 novel coronavirus (SARS-CoV-2) led to increased public and personal emphasis on such preventive measures, including face mask use. Even though face mask detection has been studied extensively, most previous works were limited to binary classification (wearing mask vs. No mask) and do not notice how masks are being improperly used. To bridge these gaps, in this study, we proposed a deep learning framework: the deep convolution neural network (CNN) combined with MobileNet V3 improved by Squeeze and Excitation blocks (SE-Mobilenet V3). The present work is new in two respects; first, it presents a multi-class detection framework with the ability to report correct as well as incorrect and out of vocabulary mark usage. second, it adopts integration of SE block to MobileNet V3 which enforces the feature representation strength and improves model robustness tremendously. Experiments conducted on a publicly available Kaggle dataset demonstrate that MobileNet V3 achieves 99% accuracy, surpassing the DCNN model’s 96%. Our proposed system implemented using convolution neural network which uses Keras,TensorFlow, and Scikit-learn to improve the accuracy of the algorithm. Beyond COVID-19, the proposed approach has significant relevance for public health monitoring, workplace safety compliance, and smart surveillance applications where adherence to protective equipment remains essential. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Multi-Class Mask Detection DCNN MobileNet V3 Squeeze and Excitation Block Deep Learning Public Health Monitoring COVID-19 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Dec, 2025 Reviews received at journal 28 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor invited by journal 09 Dec, 2025 Editor assigned by journal 05 Dec, 2025 Submission checks completed at journal 05 Dec, 2025 First submitted to journal 03 Dec, 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-8266374","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558204127,"identity":"b2cb86d0-1836-4492-a99c-5f4c00275e71","order_by":0,"name":"E 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Mask Detection, DCNN, MobileNet V3, Squeeze and Excitation Block, Deep Learning, Public Health Monitoring, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-8266374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8266374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid dissemination of the 2019 novel\u0026ensp;coronavirus (SARS-CoV-2) led to increased public and personal emphasis on such preventive measures, including face mask use. Even though face mask detection has been studied extensively, most previous works were limited to binary classification\u0026ensp;(wearing mask vs. No mask) and do not notice how masks are being improperly\u0026ensp;used. To bridge these gaps, in this study, we proposed a deep\u0026ensp;learning framework: the deep convolution neural network (CNN) combined with MobileNet V3 improved by Squeeze and Excitation blocks (SE-Mobilenet V3). The present work is new\u0026ensp;in two respects; first, it presents a multi-class detection framework with the ability to report correct as well as incorrect and out of vocabulary mark usage. second, it adopts integration of SE block to MobileNet\u0026ensp;V3 which enforces the feature representation strength and improves model robustness tremendously. Experiments conducted on a publicly available Kaggle dataset demonstrate that MobileNet V3 achieves 99% accuracy, surpassing the DCNN model\u0026rsquo;s 96%. Our proposed system implemented using convolution neural network which uses Keras,TensorFlow, and Scikit-learn to improve the accuracy of the algorithm. Beyond COVID-19, the proposed approach has significant relevance for public health monitoring, workplace safety compliance, and smart surveillance applications where adherence to protective equipment remains essential.\u003c/p\u003e","manuscriptTitle":"Automated Face Mask Detection Using Dcnn and Mobilenet V3 for Covid19 Prevention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 15:46:07","doi":"10.21203/rs.3.rs-8266374/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T02:55:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T14:48:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T05:35:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256307029342217973447049609563045228155","date":"2025-12-10T11:29:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129457017844935217339753521094411325432","date":"2025-12-10T08:47:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T00:26:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-09T18:14:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-05T13:48:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T13:46:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-03T05:57:15+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":"15d0d8c3-2656-4149-a3de-68c024b511e8","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59691109,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59691110,"name":"Physical sciences/Engineering"},{"id":59691111,"name":"Health sciences/Health care"},{"id":59691112,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-22T06:09:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 15:46:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8266374","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8266374","identity":"rs-8266374","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}