RMSeg-UDA: Unsupervised Domain Adaptation for Road Marking Segmentation Under Adverse Conditions

Abstract

The segmentation of road markings plays a crucial role in visual perception for the autonomous driving system. It enables vehicles to recognize road markings at the pixel-level, and facilitates subsequent path planning, localization, and map construction tasks. Current techniques mainly focus on normal driving scenes (i.e., clear daytime), and the performance would decrease significantly for adverse weather conditions. This work proposes RMSeg-UDA: an unsupervised domain adaptive road marking segmentation framework. By combining schedule self-training and class-conditioned adversarial training, the network utilizes both labeled normal data and unlabeled data from other domains to train a road marking segmentation model. For the evaluation on adverse conditions, a new image dataset, RLMDAC, is established with rainy and nighttime driving scenes. The experiments conducted using both public and our datasets have demonstrated the effectiveness of the proposed technique. Code and dataset are available at https://github.com/stu9113611/RMSeg-UDA.