LRS-DETR：A lightweight and efficient real-time detection algorithm for small targets of UAVs based on RT-DETR

LRS-DETR：A lightweight and efficient real-time detection algorithm for small targets of UAVs based on RT-DETR | 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 LRS-DETR：A lightweight and efficient real-time detection algorithm for small targets of UAVs based on RT-DETR Jie Xu, Huishi Luo, Zhifeng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6749392/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 address the challenges of small object detection in complex UAV environments, including dense target distribution, difficulty in feature extraction, and the limited computational resources of UAV platforms. This paper proposes LRS-DETR, a lightweight and efficient small object detection algorithm for UAV applications. First, we introduce the Feature Re-parameterized Partial Convolution (FRPCBlock) to enhance the backbone network, improving feature extraction capability while maintaining lightweight characteristics, thereby increasing computational efficiency. Then, we employ the Dynamic Position-Bias Attention Feature Interaction module (DPAFI) to enhance the model's cross-scale feature modeling ability. Next, we integrate the P2 detection layer into the ASF architecture to enable dynamic feature selection and hierarchical transmission, improving feature fusion quality through scaling and sequential processing. Finally, we employ the Focaler-Powerful-IoU regression loss function to enhance the model’s small object detection capability. Experimental results show that LRS-DETR reduces the number of parameters by 41.1% and computational complexity by 13.8%. On the VisDrone-2019 dataset, mAP0.5 and mAP0.5:0.95 increase by 2.4% and 1.7%, reaching 49.9% and 31.2%, respectively, achieving both lightweight efficiency and improved accuracy. small object detection lightweight RT-DETR feature extraction and fusion 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-6749392","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463768587,"identity":"a6578920-e1f4-449e-9072-e29aea557ec9","order_by":0,"name":"Jie Xu","email":"","orcid":"","institution":"Shanghai Polytechnic University (SSPU), Pudong New Area","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Xu","suffix":""},{"id":463768588,"identity":"bace02b4-9986-4069-aace-448e11f13055","order_by":1,"name":"Huishi Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIie3QoQ6DQAyA4RKSmyHMNoHAK0BIUBN7lGJ2Bjx25s7wDHuM6cNM3RvMnOIZUNuwE0uKm+in+ydtAYT4QykCOEpUoQ7W8RKFEIWQp02aeGIncR1ORXfDc8VMMrsg9a02CATreGckuW+R/GUw2dVFk39yFusVdtNjMLmjODKsRC/Yvd5aIVXchJpqezLtSfo6bElttifPrFuOqMO8JqosrZ3DOjKSL27nvBBCiF8+0ak3FUsuhJMAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Polytechnic University (SSPU), Pudong New Area","correspondingAuthor":true,"prefix":"","firstName":"Huishi","middleName":"","lastName":"Luo","suffix":""},{"id":463768589,"identity":"42707116-dc8c-4254-9ec1-53d4e46e35be","order_by":2,"name":"Zhifeng Wang","email":"","orcid":"","institution":"Shanghai Polytechnic University (SSPU), Pudong New Area","correspondingAuthor":false,"prefix":"","firstName":"Zhifeng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-26 09:39:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6749392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6749392/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90145445,"identity":"3a22374e-aff5-4b7a-864b-049f92087ccc","added_by":"auto","created_at":"2025-08-29 05:33:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":930015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptLRSDETR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6749392/v1_covered_1d42bbf4-05f5-4972-8fe6-a22731cec2d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"LRS-DETR：A lightweight and efficient real-time detection algorithm for small targets of UAVs based on RT-DETR","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":"small object detection, lightweight, RT-DETR, feature extraction and fusion","lastPublishedDoi":"10.21203/rs.3.rs-6749392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6749392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the challenges of small object detection in complex UAV environments, including dense target distribution, difficulty in feature extraction, and the limited computational resources of UAV platforms. This paper proposes LRS-DETR, a lightweight and efficient small object detection algorithm for UAV applications. First, we introduce the Feature Re-parameterized Partial Convolution (FRPCBlock) to enhance the backbone network, improving feature extraction capability while maintaining lightweight characteristics, thereby increasing computational efficiency. Then, we employ the Dynamic Position-Bias Attention Feature Interaction module (DPAFI) to enhance the model's cross-scale feature modeling ability. Next, we integrate the P2 detection layer into the ASF architecture to enable dynamic feature selection and hierarchical transmission, improving feature fusion quality through scaling and sequential processing. Finally, we employ the Focaler-Powerful-IoU regression loss function to enhance the model\u0026rsquo;s small object detection capability. Experimental results show that LRS-DETR reduces the number of parameters by 41.1% and computational complexity by 13.8%. On the VisDrone-2019 dataset, mAP0.5 and mAP0.5:0.95 increase by 2.4% and 1.7%, reaching 49.9% and 31.2%, respectively, achieving both lightweight efficiency and improved accuracy.\u003c/p\u003e","manuscriptTitle":"LRS-DETR：A lightweight and efficient real-time detection algorithm for small targets of UAVs based on RT-DETR","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 05:09:00","doi":"10.21203/rs.3.rs-6749392/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":"5af8b8d5-cce3-4116-901a-314486b2d85d","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-29T05:09:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 05:09:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6749392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6749392","identity":"rs-6749392","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}