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.
Abstract (translated)
无人飞行器（UAV）在基础设施检查、监控及相关任务中不可或缺，但同时也带来了关键的安全挑战。本综述对反无人机领域进行了全面的考察，重点关注三个核心目标：分类、检测和跟踪，并详细介绍了扩散数据合成、多模态融合、视觉语言建模、自监督学习以及强化学习等新兴方法。我们系统性地评估了单模态及多传感器管道（涵盖RGB图像、红外线、音频、雷达和射频）中当前最先进的解决方案，并讨论大规模及针对对抗场景的基准测试。我们的分析揭示了实时性能、隐蔽检测及集群式情景中的持久缺口，强调了开发稳健且适应性强的反无人机系统的需求。通过突出开放的研究方向，我们旨在激发创新并指导下一代防御策略的发展，在这一广泛应用UAV的时代中发挥关键作用。
URL
https://arxiv.org/abs/2504.11967
