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Drone Detection and Antidrone System Using YOLO: An Effective Approach to Airspace

Abstract:

The proliferation of drones has raised significant concerns about unauthorized incursions into restricted airspace, necessitating effective detection and neutralization strategies. This study presents a comprehensive approach integrating the YOLO (You Only Look Once) object detection algorithm with interference technology to enhance the airspace security. The YOLOv8 model, selected for its superior accuracy and robustness, was rigorously trained and evaluated in real-world scenarios to optimize drone detection. Additionally, the study incorporates the use of MDK4, a tool from the Kali Linux suite, to perform deauthentication attacks, effectively disrupting drone communication and control. Experimental results demonstrate a detection accuracy of 95% with the YOLO-based system, and a complete neutralization process averaging 2.3 s. Performance metrics such as precision, recall, and false positive/negative rates were utilized to validate the system’s effectiveness, underscoring its potential in safeguarding against aerial threats in increasingly drone-populated skies.