Small Flying Object Detection and Tracking in Digital Airport Tower through Spatial-Temporal ConvNets

Small Flying Object Detection and Tracking in Digital Airport Tower through Spatial-Temporal ConvNets | 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 Small Flying Object Detection and Tracking in Digital Airport Tower through Spatial-Temporal ConvNets Phat Thai, Sameer Alam, Nimrod Lilith, Binh Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2562253/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Digital towers using high-resolution cameras that cover a 360-degree view of airports have recently been applied as a solution for some airports by replacing conventional towers. Although many computer vision systems have been developed as tools to assist tower controllers, small flying object detection remains challenging due to their small dimensions and unpredictable trajectories. This paper proposes a novel computer vision framework to detect, track and recognize small flying objects, namely aircraft and drones, in an airport environment. The framework creates a new Convolutional Neural Network which adapts to the unique characteristics of small flying objects. It also exploits the spatial-temporal information, as well as post-processing, to improve the performance. The proposed framework is validated on an airport dataset and Drone-vs-Bird public dataset. The results show that the framework can not only perform object detection in real-time, but also surpass the performance of state-of-the-art models in both datasets by a large margin. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Software Physical sciences/Engineering/Aerospace engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 08 Aug, 2023 Reviewers agreed at journal 20 May, 2023 Reviewers agreed at journal 23 Apr, 2023 Reviewers agreed at journal 15 Apr, 2023 Reviews received at journal 12 Apr, 2023 Reviewers agreed at journal 12 Apr, 2023 Reviewers invited by journal 12 Apr, 2023 Editor assigned by journal 06 Apr, 2023 Editor invited by journal 27 Feb, 2023 Submission checks completed at journal 27 Feb, 2023 First submitted to journal 07 Feb, 2023 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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