An Improved RT-DETR Algorithm for Small-Object Detection in UAV Aerial Images

An Improved RT-DETR Algorithm for Small-Object Detection in UAV Aerial Images | 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 An Improved RT-DETR Algorithm for Small-Object Detection in UAV Aerial Images Qiyu Long, Zhixun Liang, Peng Chen, Peng Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8468217/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract To address the challenges of UAV aerial imagery, including the prevalence of small objects, complex background interference, and difficulty in feature extraction that lead to high missed detection rates and compromise detection accuracy in existing RT-DETR algorithms, this paper proposes an improved small-object-oriented detector named MSFE-DETR (Multi-Scale Feature Enhancement DETR). A CMFE (CSP-MultiScale Feature Enhancement) module is integrated into the shallow backbone layers to enhance feature representation of small objects and alleviate feature loss caused by scale and background complexity. In deeper layers of backbone, the C2f module is employed to preserve fine-grained details and improve target–background discrimination, while multi-scale feature fusion further prevents small object information degradation. In addition, Deformable Attention (DAttention) is incorporated to adaptively focus on small target regions, retaining spatial positional information and suppressing background noise. The head integrates MPCA and FSA modules, where MPCA progressively fuses adjacent-scale features to complementarily enhance small object representations and suppress background interference, and FSA further improves detail enhancement and robustness. Moreover, an Inner-SIoU loss is proposed by combining Inner-IoU with SIoU, improving localization accuracy, convergence speed, and robustness in complex scenes. Experimental results on the VisDrone 2019 dataset show that MSFE-DETR outperforms RT-DETR-r18 by 2.0% in Precision, 2.1% in Recall, and 2.4% in [email protected] , with only a slight increase in computational cost. Physical sciences/Engineering Physical sciences/Mathematics and computing UAV Object Detection Small Objects RT-DETR Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 04 Jan, 2026 Editor assigned by journal 04 Jan, 2026 Editor invited by journal 04 Jan, 2026 Submission checks completed at journal 31 Dec, 2025 First submitted to journal 31 Dec, 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. 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-8468217","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":569295964,"identity":"9d55db78-74c0-4182-8f05-abc48db5a48c","order_by":0,"name":"Qiyu Long","email":"","orcid":"","institution":"Guangxi Normal University","correspondingAuthor":false,"prefix":"","firstName":"Qiyu","middleName":"","lastName":"Long","suffix":""},{"id":569295968,"identity":"bfac013f-2655-433d-8456-f5eba79220fa","order_by":1,"name":"Zhixun 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