Abstract
Unstructured road vanishing point (VP) detection is a challenging problem, especially in the field of autonomous driving. In this article, we proposed a novel solution combining the convolutional neural network (CNN) and heatmap regression to detect unstructured road VP. The proposed algorithm first adopted a lightweight backbone, i.e., depthwise convolution-modified HRNet, to extract hierarchical features of the unstructured road image. Then, three advanced strategies, i.e., multiscale supervised learning, heatmap superresolution, and coordinate regression techniques were utilized to carry out fast and high-precision unstructured road VP detection. The empirical results on Kong's data set showed that our proposed approach had the highest detection accuracy in real-time compared with the state-of-the-art methods under various conditions, and achieved the highest speed of 33 fps.
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