ADG-YOLO: A Lightweight and Efficient Framework for Real-Time UAV Target Detection and Ranging

The rapid evolution of UAV technology has increased the demand for lightweight airborne perception systems. This study introduces ADG-YOLO, an optimized model for real-time target detection and ranging on UAV platforms. Building on YOLOv11n, we integrate C3Ghost modules for efficient feature fusion and ADown layers for detail-preserving downsampling, reducing the model's parameters to 1.77M and computation to 5.7 GFLOPs. The Extended Kalman Filter (EKF) tracking improves positional stability in dynamic environments. Monocular ranging is achieved using similarity triangle theory with known target widths. Evaluations on a custom dataset, consisting of 5,343 images from three drone types in complex environments, show that ADG-YOLO achieves 98.4% mAP0.5 and 85.2% recall at 27 FPS on Lubancat4 edge devices. Distance measurement tests indicate an average error of 4.18% in the 0.5–5 m range for the DJI NEO model, and an average error of 2.40% in the 2–50 m range for the DJI 3TD model. This work effectively bridges the gap between accuracy and efficiency for resource-constrained applications.