TRDAN: Lightweight Dual-Stream Attention Network with FPGA Acceleration for Industrial Gas Leak Detection

TRDAN: Lightweight Dual-Stream Attention Network with FPGA Acceleration for Industrial Gas Leak Detection | 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 TRDAN: Lightweight Dual-Stream Attention Network with FPGA Acceleration for Industrial Gas Leak Detection Zhaoheng Meng, He Ji, xiaofang Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7747327/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jan, 2026 Read the published version in Journal of Ambient Intelligence and Humanized Computing → Version 1 posted You are reading this latest preprint version Abstract Gas leak detection in complex industrial environments faces critical challenges such as low thermal contrast, high false-alarm rates, and real-time processing demands. To address these limitations, this study proposes a lightweight gas detection framework named Thermal-RGB Dual-stream Attention Network (TRDAN), which integrates infrared thermography with RGB visual context for FPGA-accelerated edge deployment. The architecture introduces three key innovations: (1) A dual-stream attention mechanism that enhances infrared-to-temperature variation associations, significantly improving detection accuracy and robustness; (2) An Adaptive Dynamic Feature Fusion (ADFF) module employing learnable gating coefficients to enable dynamic cross-modal interactions; (3) FPGA-implementation optimization achieving inference acceleration. Experimental results demonstrate TRDAN's superiority over state-of-the-art methods, with 23.6% higher accuracy (mAP) and 320 ns latency on Xilinx Zynq-7000. It maintains robust performance under complex environmental conditions, validated through rigorous ablation studies. The successful FPGA deployment confirms its industrial application potential, providing a high-precision, low-cost solution for edge inspection devices. Gas leak detection FPGA acceleration Infrared thermography Edge computing Industrial safety Full Text Additional Declarations No competing interests reported. Supplementary Files graficalabstract.png Cite Share Download PDF Status: Published Journal Publication published 19 Jan, 2026 Read the published version in Journal of Ambient Intelligence and Humanized Computing → 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. 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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-7747327","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528257668,"identity":"6b3d1b81-5839-47e6-9514-6a6bf3de1315","order_by":0,"name":"Zhaoheng Meng","email":"","orcid":"","institution":"Southwest University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoheng","middleName":"","lastName":"Meng","suffix":""},{"id":528257669,"identity":"8e8af277-1f42-4218-a575-56912c5a2e18","order_by":1,"name":"He Ji","email":"","orcid":"","institution":"Southwest 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Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Gas leak detection, FPGA acceleration, Infrared thermography, Edge computing, Industrial safety","lastPublishedDoi":"10.21203/rs.3.rs-7747327/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7747327/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGas leak detection in complex industrial environments faces critical challenges such as low thermal contrast, high false-alarm rates, and real-time processing demands. To address these limitations, this study proposes a lightweight gas detection framework named Thermal-RGB Dual-stream Attention Network (TRDAN), which integrates infrared thermography with RGB visual context for FPGA-accelerated edge deployment. The architecture introduces three key innovations: (1) A dual-stream attention mechanism that enhances infrared-to-temperature variation associations, significantly improving detection accuracy and robustness; (2) An Adaptive Dynamic Feature Fusion (ADFF) module employing learnable gating coefficients to enable dynamic cross-modal interactions; (3) FPGA-implementation optimization achieving inference acceleration. Experimental results demonstrate TRDAN's superiority over state-of-the-art methods, with 23.6% higher accuracy (mAP) and 320 ns latency on Xilinx Zynq-7000. It maintains robust performance under complex environmental conditions, validated through rigorous ablation studies. The successful FPGA deployment confirms its industrial application potential, providing a high-precision, low-cost solution for edge inspection devices.\u003c/p\u003e","manuscriptTitle":"TRDAN: Lightweight Dual-Stream Attention Network with FPGA Acceleration for Industrial Gas Leak Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 15:29:04","doi":"10.21203/rs.3.rs-7747327/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"88f1b5db-0a71-4f1f-b7bb-80b85dbce5d9","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:02:05+00:00","versionOfRecord":{"articleIdentity":"rs-7747327","link":"https://doi.org/10.1007/s12652-026-05041-2","journal":{"identity":"journal-of-ambient-intelligence-and-humanized-computing","isVorOnly":false,"title":"Journal of Ambient Intelligence and Humanized Computing"},"publishedOn":"2026-01-19 15:58:46","publishedOnDateReadable":"January 19th, 2026"},"versionCreatedAt":"2025-10-15 15:29:04","video":"","vorDoi":"10.1007/s12652-026-05041-2","vorDoiUrl":"https://doi.org/10.1007/s12652-026-05041-2","workflowStages":[]},"version":"v1","identity":"rs-7747327","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7747327","identity":"rs-7747327","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}