Real-Time and Multiscale Drogue and Airplane Visual Detection for Autonomous Aerial Refueling of UAVs

Autonomous aerial refueling (AAR) can improve combat radius and endurance, and the visual detection of drogue and airplane is a key technology especially for autonomous docking of unmanned aerial vehicles (UAVs). However, limited computing resources of airborne instruments, multi-scale and complex scenes seriously affect the performance and robustness of visual detection. To address the above challenges, we propose a real-time and multi-scale drogue and airplane visual detection method. Firstly, we design a lightweight backbone based on structural re-parameterization to reduce computational cost and improve inference speed. Secondly, the multi-scale feature enhancement is proposed to learn significant differences in feature scales between airplane and drogue by enabling global receptive field and encoding positional information. Finally, we design an efficient decoupled head to decouple classification and regression branches, and propose a regression loss function to avoid width-height gradient contradiction. Experimental results show that the detection accuracy of our method is superior to existing SOTA methods, and the inference speed of 128.50 FPS is achieved on embedded instruments, which verifies the robustness and real-time performance of the proposed method.