ARF-YOLO: Attention-Guided Adaptive Resolution-Aware Feature Learning for UAV Remote Sensing Object Detection

ARF-YOLO: Attention-Guided Adaptive Resolution-Aware Feature Learning for UAV Remote Sensing Object 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 ARF-YOLO: Attention-Guided Adaptive Resolution-Aware Feature Learning for UAV Remote Sensing Object Detection Long Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9299659/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Unmanned aerial vehicle (UAV)-based remote sensing object detection faces three fundamental bottlenecks: (1) insufficient resolution diversity in single-scale detection heads, causing irreversible spatial detail loss for small targets; (2) semantic gap accumulation in multi-scale feature fusion due to content-agnostic bilinear interpolation; and (3) inefficient feature resource allocation that treats all channels, spatial patches, and scale levels with equal importance regardless of relevance. To address these challenges, we propose ARF-YOLO, a novel UAV detection framework built upon YOLOv11 with three synergistic innovations. The Attention-Guided Resolution Head (AGRH) incorporates the Multi-Perspective Feature Attention (MPFA) module, which simultaneously processes dual-resolution feature streams through multi-directional pooling-based attention to fuse semantic context and fine-grained spatial cues. The Adaptive Multi-Level Feature Fusion Module (AMFF) replaces bilinear upsampling with content-adaptive dynamic kernel generation (FAUS), structure-guided feature refinement (FRS), and learning-based cross-level weighting (AFFS). The Fast Scale Resource Assigner (FSRA), adopted from the global dynamic query framework for small target detection, is incorporated into our pipeline to dynamically allocate representation capacity along channels, spatial patches, and scale levels via three lightweight parallel assigners. We further propose the ARF-Scale-Aware Loss, which amplifies supervisory signal for small objects through inverse-scale weighting. Extensive experiments on VisDrone2019 and UAVDT demonstrate that ARF-YOLO achieves 48.5% and 63.7% [email protected] respectively, surpassing the YOLOv11 baseline by 5.1 and 5.4 percentage points with only \((+)\) 2.3\,M additional parameters (11.5% relative increase) while maintaining real-time inference at 101\,fps. UAV remote sensing Object detection Attention mechanism Multi-scale feature fusion Small object detection YOLO Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 26 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 02 Apr, 2026 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-9299659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619440534,"identity":"caef2167-970a-448d-b66a-32cae4a27ff2","order_by":0,"name":"Long Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACfmbmAwYfKiR4+OUfH2BIgAga4NUi2d6WUDjjjI2cZENaAnFaDM6cMfjM25ZmbNCQA1eJXwvDjQTDzbxthxM3MJz5+OHhjsPyDOzN2yQYau7g1ME4IyHZcM65w4nbGXs3SySeOWzYwHOsTILh2DOcWpglEo4ZvCk7nLizmXcbQ2Lb4QQGiRwzCcaGwzi1sEkktv/gYQM67BjPM4gW+Tf4tfDwHGYw5AF5/wwPG9QWHvxaJNjbGAzBgTyDzRjol3TDNp60YouEY7i12B/m/wCJSgnmhx9/7rCW52c/vPHGhxrcWlABYwPQdyBGApEaIFpGwSgYBaNgFKADAN42Wl+8P9OOAAAAAElFTkSuQmCC","orcid":"","institution":"Criminal Investigation Police University of China","correspondingAuthor":true,"prefix":"","firstName":"Long","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-04-02 07:38:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9299659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9299659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107479149,"identity":"79981c2c-8749-4da9-a6c7-9f0e8a9c7828","added_by":"auto","created_at":"2026-04-22 01:20:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6693891,"visible":true,"origin":"","legend":"","description":"","filename":"ARFYOLO.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9299659/v1_covered_0151d0be-b91f-4d6b-bffe-0936703d328e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ARF-YOLO: Attention-Guided Adaptive Resolution-Aware Feature Learning for UAV Remote Sensing Object Detection","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":"journal-of-king-saud-university-computer-and-information-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University Computer and Information Sciences](https://link.springer.com/journal/44443)","snPcode":"44443","submissionUrl":"https://submission.springernature.com/new-submission/44443/3","title":"Journal of King Saud University Computer and Information Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"UAV remote sensing, Object detection, Attention mechanism, Multi-scale feature fusion, Small object detection, YOLO","lastPublishedDoi":"10.21203/rs.3.rs-9299659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9299659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnmanned aerial vehicle (UAV)-based remote sensing object detection faces three fundamental bottlenecks: (1) insufficient resolution diversity in single-scale detection heads, causing irreversible spatial detail loss for small targets; (2) semantic gap accumulation in multi-scale feature fusion due to content-agnostic bilinear interpolation; and (3) inefficient feature resource allocation that treats all channels, spatial patches, and scale levels with equal importance regardless of relevance. 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We further propose the ARF-Scale-Aware Loss, which amplifies supervisory signal for small objects through inverse-scale weighting. 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