The automotive mmWave radar plays a key role in advanced driver assistance systems (ADAS) and autonomous driving. Deep learning-based instance segmentation enables real-time object identification from the radar detection points. In the conventional training process, accurate annotation is the key. However, high-quality annotations of radar detection points are challenging to achieve due to their ambiguity and sparsity. To address this issue, we propose a contrastive learning approach for implementing radar detection points-based instance segmentation. We define the positive and negative samples according to the ground-truth label, apply the contrastive loss to train the model first, and then perform fine-tuning for the following downstream task. In addition, these two steps can be merged into one, and pseudo labels can be generated for the unlabeled data to improve the performance further. Thus, there are four different training settings for our method. Experiments show that when the ground-truth information is only available for a small proportion of the training data, our method still achieves a comparable performance to the approach trained in a supervised manner with 100% ground-truth information.

翻译：汽车毫米波雷达在高级驾驶辅助系统（ADAS）和自动驾驶中扮演着关键角色。基于深度学习的实例分割技术能够从雷达检测点中实现实时目标识别。在传统训练过程中，精确标注是关键所在。然而，由于雷达检测点具有模糊性和稀疏性，高质量标注极具挑战性。为解决这一问题，我们提出了一种基于对比学习的雷达检测点实例分割方法。根据真实标签定义正负样本，首先应用对比损失函数训练模型，随后对下游任务进行微调。此外，这两步可合并为一步，并可对未标注数据生成伪标签以进一步提升性能。由此，我们的方法共包含四种不同的训练设置。实验表明，当仅有一小部分训练数据包含真实标签信息时，我们的方法仍能达到与使用100%真实标签信息进行监督训练的方法相媲美的性能。