Detection of Micro-UAVs in Visible Spectrum Using YOLO Algorithm

Document Type : Original Article

Authors

1 Master's student of passive defense engineering, CCD, Imam Hossein University , Tehran, Iran.

2 Associate Professor, Imam Hossein Comprehensive University, Tehran, Iran.

Abstract

The micro-drone, a type of unmanned aerial vehicle, typically measures only a few centimeters and is commonly employed in military operations and espionage due to its practicality. In recent years, the field has witnessed significant threats from micro-UAVs, prompting the need for effective countermeasures. The first step in addressing this threat involves developing robust identification methods. Advances in artificial intelligence and neural networks have significantly improved the efficiency and accuracy of micro-UAV identification techniques. Utilizing artificial intelligence, micro-drones that pose a security risk to protected areas can be identified on a daily basis. One of the most widely used artificial intelligence algorithms for identifying micro-UAVs in smart devices is the YOLOv8 algorithm. In tis study, experimental results conducted on the Roboflow dataset reveals that the YOLOv8 algorithm detects micro-drones with an accuracy of 95% and a speed of 30 frames per second.

Keywords

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References

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Passive Defense
Volume 16, Issue 4 - Serial Number 64
Serial number 64. Winter 2026
February 2026
Pages 1-15

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History

  • Receive Date: 14 August 2024
  • Revise Date: 03 September 2024
  • Accept Date: 01 October 2024
  • Publish Date: 19 February 2026

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