Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation

• URL: http://arxiv.org/abs/2203.06553v1
• Date: Sun, 13 Mar 2022 03:00:34 GMT
• Title: Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation
• Authors: Weiyi Xiong, Jianan Liu, Yuxuan Xia, Tao Huang, Bing Zhu and Wei Xiang
• Abstract summary: 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 training for the following downstream task. Experiments show that when the ground-truth information is only available for 5% of the training data, our method still achieves a comparable performance to the approach trained in a supervised manner with 100% ground-truth information.
• Score: 9.491866334097114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 training 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 5% of the training data, our method still achieves a comparable performance to the approach trained in a supervised manner with 100% ground-truth information.

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