Detection of Micro-UAVs in Visible Spectrum using YOLO algorithm

	Detection of Micro-UAVs in Visible Spectrum using YOLO algorithm
پدافند غیرعامل
Articles in Press, Accepted Manuscript, Available Online from 01 October 2024
Document Type: Original Article
Authors
S. Khazai^* ¹; shahin mirzaei²
¹ihu
²IHU
Receive Date: 14 August 2024, Revise Date: 07 September 2024, Accept Date: 01 October 2024
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 the recent years, the field has witnessed significant threats from the 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 the micro-UAV identification techniques. Utilizing the 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 the smart devices is the YOLOv8 algorithm. In tis study,, the experimental results conducted on the Roboflow dataset reveals that the YOLOv8 algorithm detects micro-drones with an accuracy of 95 percent and a processing speed of 30 frames per second.
Keywords
Micro-UAV; target detection; visible spectrum; YOLO

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