IR-WSANet: An Efficient Lightweight Network for Real-Time Infrared Small Target Detection in UAV Applications

IR-WSANet: An Efficient Lightweight Network for Real-Time Infrared Small Target Detection in UAV Applications | 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 IR-WSANet: An Efficient Lightweight Network for Real-Time Infrared Small Target Detection in UAV Applications Zhan Jin, Haonan Chen, Jiaxiang Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7774389/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Infrared Small Target Detection (IRSTD) holds significant application value in military operations, early warning and surveillance, aerospace, and other fields. However, traditional detection methods face challenges related to small target pixel sizes, strong background noise, and sparse infrared image features, resulting in insufficient accuracy and robustness. This paper proposes IR-WSANet, a lightweight network based on an improved YOLOv10, which enhances the detection performance of infrared small targets through a frequency-spatial joint optimization strategy. Firstly, discrete wavelet transform convolution (DWaveletConv) is introduced into the backbone network, and the fusion of high-frequency details and low-frequency semantics is enhanced by multi-band feature decomposition to suppress noise interference; Secondly, we designed a cooperative module (POS-SHSA) that integrates POSConvEmbedding with a partial channel single-head self-attention mechanism (SHSA), which combines local spatial features and global context information to improve the positioning accuracy of small targets. Experiments verify the effectiveness of the model on SIDD and HIT-UAV datasets: the mAP of IR-WSANet on SIDD-City, SIDD-Mountain and HIT-UAV datasets reaches 97.2%, 82.6% and 82.8%, respectively, which is 2.8% to 14.1% higher than the baseline YOLOv10, and the highest F1 score was improved to 14.8%, while maintaining low computing cost (27.9 GFLOPs) and real-time performance (42.8 FPS). The results show that IR-WSANet significantly improves the detection performance of infrared small targets in complex scenes through the combination of frequency domain filtering enhancement and space-channel dual attention mechanism. Physical sciences/Engineering Physical sciences/Mathematics and computing UAV Infrared small target detection target detection IR-WSANet Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 25 Oct, 2025 Reviewers invited by journal 23 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Editor invited by journal 16 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 09 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. 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-7774389","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538607868,"identity":"295e7032-a1e8-4568-a95f-6956f4a8838c","order_by":0,"name":"Zhan Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACxmYGBmYgLcfAwAMRaCBWizHxWkAApCWxgWgtzO28hz8X1NxJ7zuee+wzD4ON7IYDzM8e4HcYX4LxjGPPcmeeeZc8m4chzXjDATZzA/xaeAySedgO5264kWPMzMNwOHHDAR42CUJaDvP8O5xuANHynygths28bYcToFoOEKXFmJm377DhzDNvjBnnGCQbzzzMZoZXi2H/GePPPN8Oy/MdzzFmeFNhJ9t3vPkZfi0NMNaBBCABCipmfOqBQB7OAmsZBaNgFIyCUYAFAAD5X0YigNlu9gAAAABJRU5ErkJggg==","orcid":"","institution":"Qiqihar University","correspondingAuthor":true,"prefix":"","firstName":"Zhan","middleName":"","lastName":"Jin","suffix":""},{"id":538607869,"identity":"3b1fedaf-7b83-4678-b45a-30b884df0290","order_by":1,"name":"Haonan Chen","email":"","orcid":"","institution":"Qiqihar University","correspondingAuthor":false,"prefix":"","firstName":"Haonan","middleName":"","lastName":"Chen","suffix":""},{"id":538607870,"identity":"afe610bb-278b-462e-93ca-5e8ba07a9778","order_by":2,"name":"Jiaxiang Chen","email":"","orcid":"","institution":"Qiqihar University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxiang","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-10-03 14:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7774389/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7774389/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94988527,"identity":"45d57ab8-4ab4-45b3-9cb0-77e9650f282d","added_by":"auto","created_at":"2025-11-03 07:09:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1213859,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscripts.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7774389/v1_covered_c3b7180c-565c-4a7d-85e9-da50435329ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"IR-WSANet: An Efficient Lightweight Network for Real-Time Infrared Small Target Detection in UAV Applications","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"UAV, Infrared small target detection, target detection, IR-WSANet","lastPublishedDoi":"10.21203/rs.3.rs-7774389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7774389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Infrared Small Target Detection (IRSTD) holds significant application value in military operations, early warning and surveillance, aerospace, and other fields. However, traditional detection methods face challenges related to small target pixel sizes, strong background noise, and sparse infrared image features, resulting in insufficient accuracy and robustness. This paper proposes IR-WSANet, a lightweight network based on an improved YOLOv10, which enhances the detection performance of infrared small targets through a frequency-spatial joint optimization strategy. Firstly, discrete wavelet transform convolution (DWaveletConv) is introduced into the backbone network, and the fusion of high-frequency details and low-frequency semantics is enhanced by multi-band feature decomposition to suppress noise interference; Secondly, we designed a cooperative module (POS-SHSA) that integrates POSConvEmbedding with a partial channel single-head self-attention mechanism (SHSA), which combines local spatial features and global context information to improve the positioning accuracy of small targets. Experiments verify the effectiveness of the model on SIDD and HIT-UAV datasets: the mAP of IR-WSANet on SIDD-City, SIDD-Mountain and HIT-UAV datasets reaches 97.2%, 82.6% and 82.8%, respectively, which is 2.8% to 14.1% higher than the baseline YOLOv10, and the highest F1 score was improved to 14.8%, while maintaining low computing cost (27.9 GFLOPs) and real-time performance (42.8 FPS). 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