Advancing Fire and Smoke Detection Reliability: Integrating Generalized ELAN and Programmable Gradient Information within YOLOv9 to Discriminate Against False Positives

Advancing Fire and Smoke Detection Reliability: Integrating Generalized ELAN and Programmable Gradient Information within YOLOv9 to Discriminate Against False Positives | 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 Advancing Fire and Smoke Detection Reliability: Integrating Generalized ELAN and Programmable Gradient Information within YOLOv9 to Discriminate Against False Positives Mehdi Nejjar, Amine Amar, Mouhcine Guennoun, Mostafa Taha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4934969/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 This paper presents YOLOv9, the advancement in the YOLO (You Only Look Once) series, innovating with the GELAN (Generalized Efficient Layer Aggregation Network) and PGI (Programmable Gradient Information) to enhance the precision of detection systems. We detail our empirical evaluation of YOLOv9 on robust and diverse datasets, highlighting its capability to maintain high detection accuracy while significantly reducing false positives, even with limited training data and in challenging scenarios. In this case, the approach has the potential to improve the accuracy of fire and smoke detection without being trained on pseudo-fire and pseudo-smoke scenes and without infrared lighting conditions. The findings highlight its potential as a reliable tool in this safety-critical application. Moreover, the proposed models demonstrate YOLOv9's superiority over its predecessors, with good performance, achieving high precision (P, mAP50, and mAP50-95) and a recall doing the inference in object early detection technologies. YOLOv9 object detection GELAN PGI fire detection smoke detection false positives infrared 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-4934969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":349693103,"identity":"75544228-5142-4e2d-947b-b1d530a6e428","order_by":0,"name":"Mehdi Nejjar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYLACCR4GBn4gfYA0LZINJGkBAQOi1es28B58YCFjl298/IzhAcYcO8JazA7wJRtI8CRbbjuTY3CAcVsyMVp4zCQkeJgNzA6AtTATpcX8hwRPvYFx/xuQlnribAGG2GEDAwmwLYeJ0HKYxxjosOMGEjeeFRxI3HacCC3Heww/S/ZUG/D3J2/+8HFbNWEtDED/Mkv2QDkJRGgAA8YPP4hVOgpGwSgYBSMSAAB67DRs+bQaxwAAAABJRU5ErkJggg==","orcid":"","institution":"Al Akhawayn University","correspondingAuthor":true,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Nejjar","suffix":""},{"id":349693104,"identity":"6d634b91-8beb-41e7-97a1-7a1ca40ccf21","order_by":1,"name":"Amine Amar","email":"","orcid":"","institution":"Al Akhawayn University","correspondingAuthor":false,"prefix":"","firstName":"Amine","middleName":"","lastName":"Amar","suffix":""},{"id":349693105,"identity":"15e3bfa6-1152-45c4-a3db-451a2ba09189","order_by":2,"name":"Mouhcine Guennoun","email":"","orcid":"","institution":"Carleton University","correspondingAuthor":false,"prefix":"","firstName":"Mouhcine","middleName":"","lastName":"Guennoun","suffix":""},{"id":349693106,"identity":"b7845412-3e25-44e3-a41b-d2344478fdd7","order_by":3,"name":"Mostafa Taha","email":"","orcid":"","institution":"Carleton University","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Taha","suffix":""}],"badges":[],"createdAt":"2024-08-18 23:09:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4934969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4934969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64083001,"identity":"79ba491a-de16-4b7e-b82b-e4f38abb9568","added_by":"auto","created_at":"2024-09-06 11:05:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5740022,"visible":true,"origin":"","legend":"","description":"","filename":"IntegratingGeneralizedELANandProgrammableGradientInformationwithinYOLOv9toDiscriminateAgainstFalsePositives.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4934969/v1_covered_978eb65e-9043-4512-aea2-a79631723f9b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAdvancing Fire and Smoke Detection Reliability: Integrating Generalized ELAN and Programmable Gradient Information within YOLOv9 to Discriminate Against False Positives\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"YOLOv9, object detection, GELAN, PGI, fire detection, smoke detection, false positives, infrared","lastPublishedDoi":"10.21203/rs.3.rs-4934969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4934969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper presents YOLOv9, the advancement in the YOLO (You Only Look Once) series, innovating with the GELAN (Generalized Efficient Layer Aggregation Network) and PGI (Programmable Gradient Information) to enhance the precision of detection systems. 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