Print Email Facebook Twitter Drone Detection & Classification with Surveillance ‘Radar On-The-Move’ and YOLO Title Drone Detection & Classification with Surveillance ‘Radar On-The-Move’ and YOLO Author Haifawi, Hani (Student TU Delft; Robin Radar Systems) Fioranelli, F. (TU Delft Microwave Sensing, Signals & Systems) Yarovoy, Alexander (TU Delft Microwave Sensing, Signals & Systems) van der Meer, Rob (Robin Radar Systems) Date 2023 Abstract A new method to jointly detect and classify drones using a moving surveillance radar system (‘radar on-the-move’) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To compensate for this, surveillance radars that offer full spatial coverage are used, but the determination of the best detection and classification approach to be applied on the resulting data is still an open challenge. In this paper a framework is proposed that combines deep learning approaches from computer vision, specifically the You Only Look Once (YOLO) network, with data from the moving surveillance radar produced by Robin Radar Systems B.V. This framework allows to jointly detect and label targets based on range-Doppler images generated in real-time. The method is validated on experimental data, with preliminary results on a small dataset showing precision, recall, mean average precision (mAP@0.5) and Area Under Curve (AUC) of over 99% Subject drone detectiondrone classificationsurveillance radarYOLO To reference this document use: http://resolver.tudelft.nl/uuid:53fccd0c-3637-45b4-bd64-f5bc14648a98 DOI https://doi.org/10.1109/RadarConf2351548.2023.10149588 Publisher IEEE, Piscataway Embargo date 2023-12-21 ISBN 978-1-6654-3670-0 Source Proceedings of the 2023 IEEE Radar Conference (RadarConf23) Event 2023 IEEE Radar Conference (RadarConf23), 2023-05-01 → 2023-05-05, San Antonio, United States Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2023 Hani Haifawi, F. Fioranelli, Alexander Yarovoy, Rob van der Meer Files PDF Drone_Detection_amp_Class ... OLO_1_.pdf 2.16 MB Close viewer /islandora/object/uuid:53fccd0c-3637-45b4-bd64-f5bc14648a98/datastream/OBJ/view