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.
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