Preprint Concept Paper Version 1 Preserved in Portico This version is not peer-reviewed

Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions

Zeeshan Kaleem
* ORCID logo ,
Misha Urooj Khan
,
Mahnoor Dil
,
Maham Misbah
,
Farooq Alam Orakzai
,
Muhammad Zeshan Alam
Version 1 : Received: 1 December 2022 / Approved: 2 December 2022 / Online: 2 December 2022 (10:33:02 CET)
Version 2 : Received: 11 January 2023 / Approved: 12 January 2023 / Online: 12 January 2023 (10:46:25 CET)

How to cite: Kaleem, Z.; Khan, M.U.; Dil, M.; Misbah, M.; Orakzai, F.A.; Alam, M.Z. Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions. Preprints 2022, 2022120049. https://doi.org/10.20944/preprints202212.0049.v1 Kaleem, Z.; Khan, M.U.; Dil, M.; Misbah, M.; Orakzai, F.A.; Alam, M.Z. Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions. Preprints 2022, 2022120049. https://doi.org/10.20944/preprints202212.0049.v1

Abstract

The emergence of Unmanned Aerial Vehicles (UAVs) raised multiple concerns, given their potentially malicious misuse in unlawful acts. Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multiclass UAV's classification performance using single-shot object detection algorithms (YOLOv5 and YOLOv7). They efficiently and correctly differentiate between multirotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the suggested technique is reliable with an overall best average-classification precision of 86.7\%, 88.5\% average recall, 91.8\% average mAP, and 58.4\% average-IoU.

Keywords

UAV detection, deep learning, YOLOv5, YOLOv7

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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