GA-Fly

The proliferation of drones across various sectors has increased the need for robust counter-drone systems to mitigate security and privacy risks. This study introduces YOLO Drone, an edge-deployable, efficient drone tracking and aiming algorithm based on YOLOv8, which is designed to enhance real time ground-to-air drone detection. The algorithm addresses the challenges of detecting small, fast-moving drones by incorporating multiscale dense connection (MDC) and multilayer shared head (MSH), optimizing YOLOv8 for superior accuracy in small target detection. To ensure operational viability on edge platforms with limited computational resources, we implement model pruning and distillation, resulting in a compact model that maintains high performance with a reduced computational load. The system also integrates a gimbal camera tracking and aiming system for dynamic target acquisition, significantly expanding the detection range beyond fixed camera limitations. The experiments revealed that YOLO-Drone improved the detection accuracy; the accuracy was 94.3%, the recall rate was 88.2%, the mAP50 was 93%, and the MAP50:95 was 54.9%, which were 0.1%, 19%, 9.6% and 6.7% higher than those of the baseline YOLOv8 model. The optimized model, deployed on the NVIDIA Jetson AGX Orin, achieves real-time processing, showcasing its potential for practical edge deployment. This research contributes a comprehensive framework for drone detection, offering a feasible solution for real-time, accurate drone tracking and aiming at edge devices. The dataset and demo video are available at https://github.com/ballballubsmart/YOLO-Drone