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Wave 雷达在高级驱动器协助系统( ADAS) 和自动驱动中发挥着关键作用。 深学习式实例分割使得能够从雷达探测点进行实时物体识别。 在常规培训过程中,准确的注解是关键。 然而, 雷达探测点的高质量说明因其模糊性和广度而难以达到。 为了解决这一问题, 我们建议采用对比式学习方法来实施雷达探测点基于实例的分解。 我们根据地面真相标签定义正和负的样本, 将对比式损失用于培训模型, 然后对下游任务进行微调。 此外, 这两个步骤可以合并为一个步骤, 并为未贴标签的数据制作假标签, 以进一步改进性能。 因此, 我们的方法有四种不同的培训环境。 实验显示, 当地面真相信息只提供给一小部分培训数据时, 我们的方法仍然取得与以100%地面真相信息监督方式培训的方法的类似性能。