Enhancing RGB-IR Object Detection: A Frozen Backbone Approach with Multi-Receptive Field Attention

Enhancing RGB-IR Object Detection: A Frozen Backbone Approach with Multi-Receptive Field Attention | 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 Enhancing RGB-IR Object Detection: A Frozen Backbone Approach with Multi-Receptive Field Attention Bingyu Lu, Haoyuan Liu, Hiroshi Watanabe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7956977/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Feb, 2026 Read the published version in The Visual Computer → Version 1 posted 13 You are reading this latest preprint version Abstract Recent advancements in multimodal object detection have predominantly relied on end-to-end training paradigms, which, while effective, demand substantial computational resources and risk feature degradation. To address these challenges, we propose a frozen backbone paradigm, preserving pretrained representations as stable semantic anchors for effcient multimodal fusion. Our approach introduces a lightweight Multi-Receptive Field Attention (MRFA) mechanism, enhancing feature interaction and representation diversity without exhaustive retraining. Experiments on the FLIR Aligned and M3 FD datasets demonstrate consistent improvements over state-of-the-art end-to-end models, highlighting the potential of pretrained backbones coupled with adaptive attention mechanisms for robust multimodal object detection. The project code is released at https://github.com/LuBingyu11/MRFA. Frozen-Backbone Multi-Receptive Attention Parameter-Free Fusion and Spatial Attention RGB-IR Object Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Feb, 2026 Read the published version in The Visual Computer → Version 1 posted Editorial decision: Revision requested 17 Dec, 2025 Reviews received at journal 15 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 28 Oct, 2025 Submission checks completed at journal 27 Oct, 2025 First submitted to journal 25 Oct, 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. 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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-7956977","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":541931779,"identity":"6f60fff4-5942-47b7-8d19-308dddef5ef6","order_by":0,"name":"Bingyu 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