UVPose: A Real-Time Key-Point-Based Skeleton Detection Network for a Drone Countermeasure System

In drone countermeasure systems, drone tracking is commonly conducted using object detection methods, which are typically limited to identifying the presence of a drone. To enhance the performance of such systems and improve the accuracy of drone flight posture prediction—while precisely capturing critical components such as rotors, mainboards, and flight trajectories—this paper introduces a novel drone key point detection model, UVPose, built upon the MMpose framework. First, we design an innovative backbone network, MDA-Net, based on the CSPNet architecture. This network improves multi-scale feature extraction and strengthens connections between low- and high-level features. To further enhance key point perception and pose estimation accuracy, a parallel attention mechanism, combining channel and spatial attention, is integrated. Next, we propose an advanced neck structure, RFN, which combines high-level semantic features from the backbone with rich contextual information from the neck. For the head, we adopt the SimCC method, optimized for lightweight, efficient, and accurate key point localization. Experimental results demonstrate that UVPose outperforms existing models, achieving a PCK of 79.2%, an AP of 67.2%, and an AR of 73.5%, with only 15.8 million parameters and 3.3 G of computation. This balance between accuracy and resource efficiency makes UVPose well suited for deployment on edge devices.