Abstract: With the rapid development of UAV technology, the application of related technologies is increasing, but it also brings many security risks and regulatory problems. As an important means to deal with these challenges, Anti-UAV detection technology has received extensive attention. Traditional UAV detection methods rely on a single modal data, such as vision, audio, radar and radio frequency signals, etc. However, these single modal data can obtain limited information in complex scenes. In recent years, deep learning methods have made good progress in the field of small object detection, and the related research of multi-modal fusion technology has further improved the accuracy and robustness of object detection. This paper reviews the research progress in the field of UAV detection, and focuses on summarizing the current research status of multimodal fusion technology. In addition, the evaluation indicators and public datasets of related UAV detection are sorted out, the limitations of existing technologies are analyzed, and the research directions for improving detection accuracy and robustness in the future are also pointed out.

Key words: multi-modal fusion, UAV technology, small object detection

摘要： 随着无人机技术的迅速发展，在相关技术应用越来越多的同时也带来了许多安全隐患和监管难题。反无人机检测技术作为应对这些挑战的重要手段，受到了广泛的关注。传统的无人机检测方法依赖于单一的模态数据，例如视觉、音频、雷达及射频信号等，但这些单一模态数据在复杂场景下所获取的信息有限。近年来，深度学习方法在小目标检测领域取得良好进展，同时多模态融合技术的相关研究也使目标检测的精度和鲁棒性得到进一步的提升。综述了无人机检测领域的研究进展，重点梳理了多模态融合技术的研究现状；此外，整理了相关无人机检测的评价指标和公开数据集，分析了现有技术的局限性，并指出了未来提升检测精度与鲁棒性的研究方向。

关键词: 多模态融合, 无人机技术, 小目标检测