1. Proceedings
  2. I3M
  3. 2021
  4. DHSS
  5. 10.46354/i3m.2021.dhss.009

Drone detection with YOLOv5

  • a Jiri Kralicek 
  • a, University of Defence, Kounicova 65, Brno, 662 10, Czech Republic
Cite as
Kralicek J. (2021). Drone detection with YOLOv5. Proceedings of the 11th International Defence and Homeland Security Simulation Worskhop (DHSS 2021), pp. 63-69. DOI: https://doi.org/10.46354/i3m.2021.dhss.009
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Abstract

We propose a fast and accurate method for visual drone detection based on YOLOv5 architecture providing state-of-the-art performance. The proposed method aims to drone detection in combat and real-world environments for military use based on visual detection in the visible and infrared spectrum. The method provides precision/recall of 99.1/98.5% and 99.0/95.3% for RGB and infrared videos from the AntiUAV dataset.

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