用双毫米波雷达实现厘米级精度的实时无人机3D检测
CubeDN: Real-time Drone Detection in 3D Space from Dual mmWave Radar Cubes
- 采用双雷达配置+端到端深度学习，提升垂直方向分辨率
- 在近距离实现分米级定位，平均精度95%，召回率85%
- 每秒处理10帧，适合实际部署场景
随着无人机使用日益普及，保障安全与安防成为关键需求。现有光学传感器如摄像头和LiDAR在恶劣光照或环境下性能下降，促使研究转向毫米波（mmWave）雷达。当前多数mmWave研究聚焦于二维道路使用者检测，缺乏对高度信息的感知能力，难以满足三维无人机检测需求。为此，本文提出CubeDN——一种专为飞行无人机设计的单阶段端到端雷达目标检测网络。通过双雷达配置与创新深度学习流程，有效克服俯仰角分辨率低的问题，可同时完成无人机的检测、定位与分类，近距下实现分米级跟踪精度，整体平均精度达95%，平均召回率达85%。系统推理速度达10Hz，具备实际应用潜力。
原文摘要 · Abstract (English)
As drone use has become more widespread, there is a critical need to ensure safety and security. A key element of this is robust and accurate drone detection and localization. While cameras and other optical sensors like LiDAR are commonly used for object detection, their performance degrades under adverse lighting and environmental conditions. Therefore, this has generated interest in finding more reliable alternatives, such as millimeter-wave (mmWave) radar. Recent research on mmWave radar object detection has predominantly focused on 2D detection of road users. Although these systems demonstrate excellent performance for 2D problems, they lack the sensing capability to measure elevation, which is essential for 3D drone detection. To address this gap, we propose CubeDN, a single-stage end-to-end radar object detection network specifically designed for flying drones. CubeDN overcomes challenges such as poor elevation resolution by utilizing a dual radar configuration and a novel deep learning pipeline. It simultaneously detects, localizes, and classifies drones of two sizes, achieving decimeter-level tracking accuracy at closer ranges with overall $95\%$ average precision (AP) and $85\%$ average recall (AR). Furthermore, CubeDN completes data processing and inference at 10Hz, making it highly suitable for practical applications.
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