Object detection using FMCW (Frequency-modulated continuous wave) radars is of massive importance for the advanced driver assistance systems. However, it is exceptionally challenging due to the diversity of the electromagnetic environment and the existence of the class imbalance in the radar data space. In this paper, we propose a cascaded object detection network to achieve accurate object detection using FMCW radars. Consisting of a ROI generation stage and a final detection stage, the proposed cascaded network can tackle the problem of the class imbalance and detect objects from the range-Doppler or range-velocity space effectively. Besides, we propose a range-velocity regression procedure to improve the performance of the range-velocity localization. Extensive simulation experiments demonstrate that our proposed approach can robustly detect objects from noisy electromagnetic environments with a high localization accuracy.

使用FMCW（调频连续波）雷达进行目标检测对于高级驾驶员辅助系统具有极为重要的意义。但是，由于电磁环境的多样性以及雷达数据空间中类别不平衡的存在，这是极具挑战性的。在本文中，我们提出了一种级联的目标检测网络，以使用FMCW雷达实现精确的目标检测。所提出的级联网络由ROI生成阶段和最终检测阶段组成，可以解决类不平衡的问题，并能有效地从距离多普勒或距离速度空间中检测物体。此外，我们提出了一种距离速度回归程序，以提高距离速度定位的性能。