Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation
Paper in proceeding, 2022

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.


Weiyi Xiong

Beihang University

Jianan Liu

Silo AI

Vitalent Consulting

Yuxuan Xia

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Tao Huang

James Cook University

Bing Zhu

Beihang University

Wei Xiang

La Trobe University

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

Vol. 2022-October 1255-1261
9781665468800 (ISBN)

25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
Macau, China,

Subject Categories

Other Computer and Information Science

Computer Science

Computer Vision and Robotics (Autonomous Systems)



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