Among various sensors for assisted and autonomous driving systems, automotive radar has been considered as a robust and low-cost solution even in adverse weather or lighting conditions. With the recent development of radar technologies and open-sourced annotated data sets, semantic segmentation with radar signals has become very promising. However, existing methods are either computationally expensive or discard significant amounts of valuable information from raw 3D radar signals by reducing them to 2D planes via averaging. In this work, we introduce ERASE-Net, an Efficient RAdar SEgmentation Network to segment the raw radar signals semantically. The core of our approach is the novel detect-then-segment method for raw radar signals. It first detects the center point of each object, then extracts a compact radar signal representation, and finally performs semantic segmentation. We show that our method can achieve superior performance on radar semantic segmentation task compared to the state-of-the-art (SOTA) technique. Furthermore, our approach requires up to 20x less computational resources. Finally, we show that the proposed ERASE-Net can be compressed by 40% without significant loss in performance, significantly more than the SOTA network, which makes it a more promising candidate for practical automotive applications.

中文翻译：

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ERASE-Net：汽车雷达信号的高效分割网络

在用于辅助和自动驾驶系统的各种传感器中，即使在恶劣的天气或照明条件下，汽车雷达也被认为是一种强大且低成本的解决方案。随着雷达技术和开源注释数据集的最新发展，利用雷达信号进行语义分割变得非常有前途。然而，现有方法要么计算成本高昂，要么通过平均将原始 3D 雷达信号减少到 2D 平面，从而丢弃大量有价值的信息。在这项工作中，我们引入了 ERASE-Net，这是一种高效的雷达分割网络，用于对原始雷达信号进行语义分割。我们方法的核心是用于原始雷达信号的新型检测然后分割方法。它首先检测每个物体的中心点，然后提取一个紧凑的雷达信号表示，最后进行语义分割。我们表明，与最先进的 (SOTA) 技术相比，我们的方法可以在雷达语义分割任务上实现卓越的性能。此外，我们的方法需要最多 20 倍的计算资源。最后，我们展示了所提出的 ERASE-Net 可以压缩 40% 而不会显着降低性能，明显超过 SOTA 网络，这使其成为实际汽车应用中更有希望的候选者。