Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions
Yifei Dong, Fengyi Wu, Sanjian Zhang, Guangyu Chen, Yuzhi Hu, Masumi Yano, Jingdong Sun, Siyu Huang, Feng Liu, Qi Dai, Zhi-Qi Cheng
TL;DR
This paper surveys the anti-UAV landscape, focusing on classification, detection, and tracking across multi-modal sensing. It synthesizes available UAV datasets and benchmarking practices, and reviews representative methods across RF, audio, radar, vision, and multi-modal modalities. It highlights emerging directions—diffusion-based data synthesis, vision-language models, self-supervised learning, multi-modal fusion, and reinforcement learning—as avenues to address real-time, adversarial, and swarm-scale challenges. The authors argue for robust, adaptive systems and richer benchmarks to advance field deployment.
Abstract
Unmanned Aerial Vehicles (UAVs) are indispensable for infrastructure inspection, surveillance, and related tasks, yet they also introduce critical security challenges. This survey provides a wide-ranging examination of the anti-UAV domain, centering on three core objectives-classification, detection, and tracking-while detailing emerging methodologies such as diffusion-based data synthesis, multi-modal fusion, vision-language modeling, self-supervised learning, and reinforcement learning. We systematically evaluate state-of-the-art solutions across both single-modality and multi-sensor pipelines (spanning RGB, infrared, audio, radar, and RF) and discuss large-scale as well as adversarially oriented benchmarks. Our analysis reveals persistent gaps in real-time performance, stealth detection, and swarm-based scenarios, underscoring pressing needs for robust, adaptive anti-UAV systems. By highlighting open research directions, we aim to foster innovation and guide the development of next-generation defense strategies in an era marked by the extensive use of UAVs.