Advancements in Feature Fusion, Enhancement Techniques, and Loss Function Optimization for Infrared and Visible Light Fusion Using the YOLOv8 Framework

Advancements in Feature Fusion, Enhancement Techniques, and Loss Function Optimization for Infrared and Visible Light Fusion Using the YOLOv8 Framework | 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 Advancements in Feature Fusion, Enhancement Techniques, and Loss Function Optimization for Infrared and Visible Light Fusion Using the YOLOv8 Framework Wenyuan Xu, Shuai Li, Yongcheng Ji, Xiang Li, Chuang Cui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4303883/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 To mitigate the parameter increase from integrating infrared data and enhance detection accuracy, this work introduces an advanced fusion framework for visible and infrared thermal imaging. It presents the refined C2fv1k9 module and the MA4CBCA feature enhancement module, leveraging a hybrid attention mechanism. Additionally, the CorAF2n1 feature fusion module, utilizing an attention mechanism, and enhancements to the CIOU loss function's penalty term are proposed. This culminates in conducting experiments and demonstrations using the model yolov8n_f4s2c_m4ca2n1_cdiou5_cdiou5. Relative to the previously examined yolov8n_f4_scaff2_adf model, this model's accuracy improved to 0.924 from 0.885, recall rate to 0.916 from 0.876, and mAP@50–95 significantly increased to 0.728 from 0.711. These enhancements not only underscore the model's superiority in accuracy and reliability but also demonstrate its capacity for delivering exceptional detection performance with minimal computational resources. Physical sciences/Engineering/Civil engineering Physical sciences/Mathematics and computing/Computer science 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-4303883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":297452252,"identity":"765c9b12-d30a-47d8-8790-235f8ae07349","order_by":0,"name":"Wenyuan Xu","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Wenyuan","middleName":"","lastName":"Xu","suffix":""},{"id":297452254,"identity":"006fc1b8-f692-4886-a2a7-3d1efe4894a9","order_by":1,"name":"Shuai Li","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Li","suffix":""},{"id":297452256,"identity":"12ef9812-c3d2-42e8-b6d6-78f03377a038","order_by":2,"name":"Yongcheng Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYLACxgYQebDxQUKFDWlamg0enEkjSQsDm+TDtkOEVRsc7z384ucOmzx5x8NtFQlsBxj427sT8Gs5cy7NsvdMWrHhgYNtNxJ47jBInDm7Ab+WGzlmxoxthxM3NoC0SDxjMJDIJUrLf7CWggSDw0RpMX7M2HYgcT7DwTaGhAQitEieOWPG2NuWnLgBGMgSCQfSeAj6he94j/GHn212ifNnHH/48ec/Gzn+9l78WhQOMLBJQFx4ACzAg1c5CMg3MDB/ADP6GwgqHgWjYBSMghEKAOnrWBMBYe78AAAAAElFTkSuQmCC","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Yongcheng","middleName":"","lastName":"Ji","suffix":""},{"id":297452258,"identity":"c27fa8cb-027c-4ee5-827d-5f6541b56ac1","order_by":3,"name":"Xiang Li","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""},{"id":297452259,"identity":"95d51d36-93da-40f4-a759-6273148d5056","order_by":4,"name":"Chuang Cui","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Chuang","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2024-04-22 07:23:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4303883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4303883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64814610,"identity":"fd05c733-06ef-4160-b97f-73b6dbf46889","added_by":"auto","created_at":"2024-09-19 06:30:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":868617,"visible":true,"origin":"","legend":"","description":"","filename":"AdvancementsinFeatureFusionEnhancementTechniquesandLossFunctionOptimizationforInfraredandVisibleLightFusionUsingtheYOLOv8Framework.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4303883/v1_covered_c59fd9fe-960b-48fb-b157-4c44014acc41.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advancements in Feature Fusion, Enhancement Techniques, and Loss Function Optimization for Infrared and Visible Light Fusion Using the YOLOv8 Framework","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4303883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4303883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo mitigate the parameter increase from integrating infrared data and enhance detection accuracy, this work introduces an advanced fusion framework for visible and infrared thermal imaging. It presents the refined C2fv1k9 module and the MA4CBCA feature enhancement module, leveraging a hybrid attention mechanism. Additionally, the CorAF2n1 feature fusion module, utilizing an attention mechanism, and enhancements to the CIOU loss function's penalty term are proposed. This culminates in conducting experiments and demonstrations using the model yolov8n_f4s2c_m4ca2n1_cdiou5_cdiou5. Relative to the previously examined yolov8n_f4_scaff2_adf model, this model's accuracy improved to 0.924 from 0.885, recall rate to 0.916 from 0.876, and mAP@50\u0026ndash;95 significantly increased to 0.728 from 0.711. 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