Semi-supervised vanishing point detection with contrastive learning

Abstract

Vanishing point (VP) detection in road images plays an important role in driving scenes for advanced driver assistance systems (ADAS) and autonomous vehicles. Because it is still a challenging problem to obtain expensive annotated datasets in deep-learning-based supervised training, in this paper, we propose a semi-supervised VP detection method in road images. Our proposed model first extracts high-resolution VP heatmaps of the road images by fusing multi-level and global–local contrastive learning. Then, we apply the π model as a semi-supervised learning framework and feed the whole global network with a small number of training samples to detect VPs. In the experiment, we used Kong’s dataset for the unstructured road test and the KITTI-VP dataset for the structured road test. Compared with the state-of-the-art methods, our model achieved high accuracy and robustness in road VP detection.