Lightweight Anti-UAV Object Tracking With Visual Sensing Based on Heterogeneous Model Knowledge Distillation
Feng Cheng, Gaoliang Peng, Hang Li, Benqi Zhao, Rui Li · IEEE Sensors Journal · 2025
The widespread application of Unmanned Aerial Vehicles (UAVs) presents substantial challenges to public safety, thereby necessitating accurate visual tracking for effective counter-UAV operations. However, most existing transformer-based trackers rely on increasingly large and deep networks to improve accuracy in complex backgrounds, which constrains their practicality in real-world deployments. To address this limitation, we propose a lightweight anti‑UAV tracker based on knowledge distillation. Specifically, we design a fully transformer-based anti‑UAV teacher network (TransADT) and develop a lightweight Siamese student network (SiamGLT) constructed with on a gated linear transformer, enabling accurate real-time tracking. In addition, we introduce a heterogeneous model knowledge distillation framework (HKD) to facilitate knowledge transfer from the large transformer teacher to the convolutional student network. This framework comprises three types of knowledge transfer: foreground and background feature decoupled distillation, instance-wise relation distillation, and response knowledge distillation. Comprehensive experiments on Anti-UAV benchmarks and real-world scenarios demonstrate that the proposed tracker surpasses other state-of-the-art methods. Our SiamGLT model achieves an optimal balance between tracking accuracy and computational efficiency.