Enhanced YOLOv11n for Small Object Detection in UAV Imagery: Higher Accuracy with Fewer Parameters

Enhanced YOLOv11n for Small Object Detection in UAV Imagery: Higher Accuracy with Fewer Parameters | 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 Enhanced YOLOv11n for Small Object Detection in UAV Imagery: Higher Accuracy with Fewer Parameters Hongkai zhu, Xianghua Xie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7553905/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Object detection in UAV imagery faces several challenges due to high-altitude aerial capture: targets are densely distributed, small objects account for a large proportion, and onboard computing power is limited, leading to low detection accuracy and high rates of false and missed detections. To address these issues, this article proposes an improved YOLOv11 model. First, we design a Multiscale Edge-Feature Adaptive Selection (MSEAF) module in the backbone to effectively cope with the predominance of small objects and weak edge information. Second, we use ScalCat and Scal3DC modules to reconstruct the neck and add a P2 small object detection head, alleviating feature degradation in multiscale processing and improving high-resolution information utilization. Finally, we design a shared, reparameterized lightweight detection head (SRepD) to resolve computational redundancy and insufficient feature fusion in conventional heads. The experimental results show that, compared to the YOLOv11n baseline, our model increases mAP50 and precision by 4.6% while reducing the parameters by approximately 8.5%. On datasets containing extremely small object categories, our model improves mAP50 and precision by 5.5% and 5.6%, respectively, with a 7.7% reduction in parameters relative to YOLOv11n. Compared with the larger YOLOv11s, our model achieves gains of 3.8% in mAP50 and 3.2% in precision while using only 25% of its parameters, demonstrating cross-scale performance superiority. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviews received at journal 08 Oct, 2025 Reviewers agreed at journal 05 Oct, 2025 Reviewers invited by journal 01 Oct, 2025 Editor assigned by journal 23 Sep, 2025 Editor invited by journal 23 Sep, 2025 Submission checks completed at journal 18 Sep, 2025 First submitted to journal 18 Sep, 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. 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Parameters","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7553905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7553905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Object detection in UAV imagery faces several challenges due to high-altitude aerial capture: targets are densely distributed, small objects account for a large proportion, and onboard computing power is limited, leading to low detection accuracy and high rates of false and missed detections. To address these issues, this article proposes an improved YOLOv11 model. First, we design a Multiscale Edge-Feature Adaptive Selection (MSEAF) module in the backbone to effectively cope with the predominance of small objects and weak edge information. Second, we use ScalCat and Scal3DC modules to reconstruct the neck and add a P2 small object detection head, alleviating feature degradation in multiscale processing and improving high-resolution information utilization. Finally, we design a shared, reparameterized lightweight detection head (SRepD) to resolve computational redundancy and insufficient feature fusion in conventional heads. The experimental results show that, compared to the YOLOv11n baseline, our model increases mAP50 and precision by 4.6% while reducing the parameters by approximately 8.5%. On datasets containing extremely small object categories, our model improves mAP50 and precision by 5.5% and 5.6%, respectively, with a 7.7% reduction in parameters relative to YOLOv11n. Compared with the larger YOLOv11s, our model achieves gains of 3.8% in mAP50 and 3.2% in precision while using only 25% of its parameters, demonstrating cross-scale performance superiority.","manuscriptTitle":"Enhanced YOLOv11n for Small Object Detection in UAV Imagery: Higher Accuracy with Fewer Parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 11:17:31","doi":"10.21203/rs.3.rs-7553905/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-27T12:44:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-19T09:42:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262196586259196561060981627466085700443","date":"2025-10-14T04:36:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-08T09:24:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295602203507390217860008122874211273286","date":"2025-10-05T06:51:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-01T16:36:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T02:12:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-23T17:43:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T20:10:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-18T12:22:42+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"b93b0ec6-8897-434a-b5f0-78c933d89703","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56244683,"name":"Physical sciences/Engineering"},{"id":56244684,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-19T16:48:20+00:00","versionOfRecord":{"articleIdentity":"rs-7553905","link":"https://doi.org/10.1038/s41598-026-35301-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-18 16:29:31","publishedOnDateReadable":"January 18th, 2026"},"versionCreatedAt":"2025-10-15 11:17:31","video":"","vorDoi":"10.1038/s41598-026-35301-2","vorDoiUrl":"https://doi.org/10.1038/s41598-026-35301-2","workflowStages":[]},"version":"v1","identity":"rs-7553905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7553905","identity":"rs-7553905","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}