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Securing the Skies: a Comprehensive Survey on Anti-Uav Methods, Benchmarking, and Future Directions
Conference proceeding

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, …
IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops, pp 6661-6675
11 Jun 2025
DOI: https://doi.org/10.1109/CVPRW67362.2025.00663

Abstract

anti-uav Benchmark testing multi-modal fusion Real-time systems Reinforcement learning Self-supervised learning Sensor fusion Surveillance Surveys Technological innovation uav classification uav detection uav tracking Radar Tracking Semantics
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.

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