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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.11967 |
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| _version_ | 1866918413681557504 |
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| author | Dong, Yifei Wu, Fengyi Zhang, Sanjian Chen, Guangyu Hu, Yuzhi Yano, Masumi Sun, Jingdong Huang, Siyu Liu, Feng Dai, Qi Cheng, Zhi-Qi |
| author_facet | Dong, Yifei Wu, Fengyi Zhang, Sanjian Chen, Guangyu Hu, Yuzhi Yano, Masumi Sun, Jingdong Huang, Siyu Liu, Feng Dai, Qi Cheng, Zhi-Qi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11967 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions Dong, Yifei Wu, Fengyi Zhang, Sanjian Chen, Guangyu Hu, Yuzhi Yano, Masumi Sun, Jingdong Huang, Siyu Liu, Feng Dai, Qi Cheng, Zhi-Qi Computer Vision and Pattern Recognition Artificial Intelligence Robotics 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. |
| title | Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2504.11967 |