Bio-inspired Motion Detection Models for Improved Uav and Bird Differentiation: a Novel Deep Learning Framework

Bio-inspired Motion Detection Models for Improved Uav and Bird Differentiation: a Novel Deep Learning Framework | 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 Bio-inspired Motion Detection Models for Improved Uav and Bird Differentiation: a Novel Deep Learning Framework Najiba Said Hamed Al-Zadjali, Sundaravadivazhagan Balasubaramanian, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5508995/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 May, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract The rapid increase in Unmanned Aerial Vehicle (UAV) deployments has led to growing concerns about their detection and differentiation from birds, particularly in sensitive areas like airports. Existing detection systems often struggle to distinguish between UAVs and birds due to their similar flight patterns, resulting in high false positive rates and missed detections. This research presents a bio-inspired deep learning model, the Spatiotemporal Bio-Response Neural Network (STBRNN), designed to enhance the differentiation between UAVs and birds in real-time. The model consists of three core components: a Bio-Inspired Convolutional Neural Network (Bio-CNN) for spatial feature extraction, Gated Recurrent Units (GRUs) for capturing temporal motion dynamics, and a novel Bio-Response Layer that adjusts attention based on movement intensity, object proximity, and velocity consistency. The dataset used includes labeled images and videos of UAVs and birds captured in various environments, processed following YOLOv7 specifications. Extensive experiments were conducted comparing STBRNN with five state-of-the-art models, including YOLOv5, Faster R-CNN, SSD, RetinaNet, and R-FCN. The results demonstrate that STBRNN achieves superior performance across multiple metrics, with a precision of 0.984, recall of 0.964, F1 score of 0.974, and an IoU of 0.96. Additionally, STBRNN operates at an inference time of 45ms per frame, making it highly suitable for real-time applications in UAV and bird detection. Earth and environmental sciences/Environmental social sciences Physical sciences/Engineering Unmanned Aerial Vehicle (UAV) Spatiotemporal Bio-Response Neural Network (STBRNN) Bio-Inspired Convolutional Neural Network (Bio-CNN) Gated Recurrent Units (GRUs) YOLOv5 Faster R-CNN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 23 Apr, 2025 Reviews received at journal 19 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviewers agreed at journal 11 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 09 Apr, 2025 Submission checks completed at journal 07 Apr, 2025 First submitted to journal 25 Mar, 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. 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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-5508995","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440642587,"identity":"d93cb1b2-94ed-4ee2-8e23-35e3413ddbd5","order_by":0,"name":"Najiba Said Hamed Al-Zadjali","email":"","orcid":"","institution":"University of Technology and Applied Sciences-AL Mussanah","correspondingAuthor":false,"prefix":"","firstName":"Najiba","middleName":"Said Hamed","lastName":"Al-Zadjali","suffix":""},{"id":440642588,"identity":"27477fcd-ff2e-4abe-a6fe-d061f3abdfe6","order_by":1,"name":"Sundaravadivazhagan 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