EBAD-YOLO: Efficient Bidirectional Adaptive Dense Network for UAV Small Object Detection

EBAD-YOLO: Efficient Bidirectional Adaptive Dense Network for UAV Small 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 EBAD-YOLO: Efficient Bidirectional Adaptive Dense Network for UAV Small Object Detection Shuo Hu, Run Xing, Liyang Han, Tongtong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7578170/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Journal of Real-Time Image Processing → Version 1 posted 14 You are reading this latest preprint version Abstract Object detection in drone imagery faces substantial challenges in achieving both high accuracy and computational efficiency for on-board deployment. To address these issues, we propose EBAD-YOLO (Efficient Bidirectional Adaptive Dense YOLO), an enhanced architecture built upon YOLOv10. Specifically, we propose a Bidirectional Adaptive Dense Connection Feature Pyramid Network (BADC-FPN) that facilitates effective multi-scale feature fusion through bidirectional cross-scale dense connections and adaptive weighting mechanisms. Building on this, we improve efficiency by optimizing C2f with the Fasterblock module. Additionally, a Localization Quality Estimation (LQE) module is incorporated into the detection head to suppress low-quality predictions, thus optimizing the precision-recall trade-off. Finally, layer-adaptive magnitude-based pruning (LAMP) is employed to further compress the model, ensuring efficient deployment. Evaluations on the VisDrone2019 dataset demonstrate that EBAD-YOLO enhances mAP@50 by 3.6 % compared to YOLOv10s, while reducing GFLOPs by 56.5% and the number of parameters by 66.7%. Its robust generalization capability is further validated on the TinyPerson and LEVIR-Ship datasets. Deep learning Small object detection YOLO Feature Pyramid Network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Journal of Real-Time Image Processing → Version 1 posted Editorial decision: Revision requested 21 Sep, 2025 Reviews received at journal 20 Sep, 2025 Reviews received at journal 19 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 10 Sep, 2025 Submission checks completed at journal 10 Sep, 2025 First submitted to journal 09 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. 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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-7578170","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518295793,"identity":"aef679f1-bc92-4bf1-a922-c5da42ce7d87","order_by":0,"name":"Shuo 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To address these issues, we propose EBAD-YOLO (Efficient Bidirectional Adaptive Dense YOLO), an enhanced architecture built upon YOLOv10. \nSpecifically, we propose a Bidirectional Adaptive Dense Connection Feature Pyramid Network (BADC-FPN) that facilitates effective multi-scale feature fusion through bidirectional cross-scale dense connections and adaptive weighting mechanisms. Building on this, we improve efficiency by optimizing C2f with the Fasterblock module. Additionally, a Localization Quality Estimation (LQE) module is incorporated into the detection head to suppress low-quality predictions, thus optimizing the precision-recall trade-off. Finally, layer-adaptive magnitude-based pruning (LAMP) is employed to further compress the model, ensuring efficient deployment. \nEvaluations on the VisDrone2019 dataset demonstrate that EBAD-YOLO enhances mAP@50 by 3.6 \\% compared to YOLOv10s, while reducing GFLOPs by 56.5\\% and the number of parameters by 66.7\\%. 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