Real-time detection of fires and smoke in healthcare facilities using advanced deep learning models on live video streams of surveillance cameras

Real-time detection of fires and smoke in healthcare facilities using advanced deep learning models on live video streams of surveillance cameras | 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 Real-time detection of fires and smoke in healthcare facilities using advanced deep learning models on live video streams of surveillance cameras Mostafa Rizk, Houssein Taleb, Ali Rhayem, Jad Abou Chaaya, Chamseddine Zaki, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8815873/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fires in healthcare facilities pose a critical risk to patients and staff, yet conventional detection systems often respond slowly and cannot operate in real time. This study proposes an AI-driven fire and smoke detection system leveraging existing CCTV networks and advanced deep learning models for rapid hazard recognition.We focus on YOLOv11, the latest in the YOLO object detection family, and benchmark its performance against YOLOv8. A custom dataset of 17,525 images with 27,314 annotated fire and smoke instances was compiled, encompassing diverse indoor and outdoor scenarios. All YOLOv11 variants (nano to extra-large) were trained and evaluated, achieving high detection accuracy, with the medium model reaching a mean average precision (mAP@50) of 90%. The results highlight how model size affects detection speed and accuracy, demonstrating the feasibility of deploying AI-based real-time fire detection systems in healthcare environments to enhance safety and minimize false alarms. Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Deep Learning Fire Detection Healthcare Smoke Detection YOLO Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-8815873","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":610486758,"identity":"f4d21c3a-ad77-4f3d-9827-9e0cf74e8e60","order_by":0,"name":"Mostafa Rizk","email":"","orcid":"","institution":"Lebanese University","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Rizk","suffix":""},{"id":610486759,"identity":"89847359-4507-4f88-a6cf-585aea37ee8d","order_by":1,"name":"Houssein Taleb","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACAwglASIYDzA22ADpAwwMPAwMMgYEtbAB1TI2pMG18BDQwgDTchjCwafFnL3H+OOPCos8BvnmBwc+7jifOL/xAOODt20MPOY4tFj2nDGT5jkjUczAxmZwcOaZ24kbDhxgNpwL1GLZgMNhN3LMmBnbJBIb2BgMDvO2AbUwHGCT5gVqMTiAU4vxx5//QFrYPxz+23YucX7DAfbfBLQYSPA2gLTwGBxmbDuQ2HDgABszXi1njpVJ8xyTSGxjyyk42Hsm2XjDgYPNknPOSeD2y/HmzR9/1NQl9jMf3/jg5w472fkzDh/88KbMRg5XiMEBG5wlcRBkvAQhDciAH4eDRsEoGAWjYMQCAKMJYP7cR5M2AAAAAElFTkSuQmCC","orcid":"","institution":"Saint Joseph University","correspondingAuthor":true,"prefix":"","firstName":"Houssein","middleName":"","lastName":"Taleb","suffix":""},{"id":610486760,"identity":"efa9f6e2-9691-487e-a0b1-5ad2ed85329b","order_by":2,"name":"Ali Rhayem","email":"","orcid":"","institution":"Lebanese University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Rhayem","suffix":""},{"id":610486761,"identity":"8dcf8753-e63e-46f6-bea9-74a399c60d89","order_by":3,"name":"Jad Abou Chaaya","email":"","orcid":"","institution":"UMR CNRS 6285, ENIB","correspondingAuthor":false,"prefix":"","firstName":"Jad","middleName":"Abou","lastName":"Chaaya","suffix":""},{"id":610486762,"identity":"b82a3318-fe90-48cd-a3c9-b2eea8d8deb7","order_by":4,"name":"Chamseddine Zaki","email":"","orcid":"","institution":"American University of the Middle East","correspondingAuthor":false,"prefix":"","firstName":"Chamseddine","middleName":"","lastName":"Zaki","suffix":""},{"id":610486763,"identity":"3ab276b8-299e-4c47-97e3-c8c07e3b2d36","order_by":5,"name":"Abbass Nasser","email":"","orcid":"","institution":"Holy Spirit University of Kaslik","correspondingAuthor":false,"prefix":"","firstName":"Abbass","middleName":"","lastName":"Nasser","suffix":""}],"badges":[],"createdAt":"2026-02-07 13:39:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8815873/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8815873/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107302119,"identity":"17d2c958-7a5c-4ac8-b2f3-627f1b180961","added_by":"auto","created_at":"2026-04-20 07:43:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1917433,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificreportsFiredetectionpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8815873/v1_covered_8ef032ba-b199-49d0-ad58-c2657becf29c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-time detection of fires and smoke in healthcare facilities using advanced\ndeep learning models on live video streams of surveillance cameras","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deep Learning, Fire Detection, Healthcare, Smoke Detection, YOLO","lastPublishedDoi":"10.21203/rs.3.rs-8815873/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8815873/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFires in healthcare facilities pose a critical risk to patients and staff, yet conventional detection systems often respond slowly and cannot operate in real time. 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