Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression

• URL: http://arxiv.org/abs/2006.04691v1
• Date: Mon, 8 Jun 2020 15:44:37 GMT
• Title: Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression
• Authors: Yin-Bo Liu, Ming Zeng, Qing-Hao Meng
• Abstract summary: We propose a novel solution combining the convolutional neural network (CNN) and heatmap regression to detect unstructured road VP. The proposed algorithm firstly adopts a lightweight backbone, i.e., depthwise convolution modified HRNet, to extract hierarchical features of the unstructured road image. Three advanced strategies, i.e., multi-scale supervised learning, heatmap super-resolution, and coordinate regression techniques are utilized to achieve fast and high-precision unstructured road VP detection.
• Score: 3.8170259685864165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unstructured road vanishing point (VP) detection is a challenging problem, especially in the field of autonomous driving. In this paper, we proposed a novel solution combining the convolutional neural network (CNN) and heatmap regression to detect unstructured road VP. The proposed algorithm firstly adopts a lightweight backbone, i.e., depthwise convolution modified HRNet, to extract hierarchical features of the unstructured road image. Then, three advanced strategies, i.e., multi-scale supervised learning, heatmap super-resolution, and coordinate regression techniques are utilized to achieve fast and high-precision unstructured road VP detection. The empirical results on Kong's dataset show that our proposed approach enjoys the highest detection accuracy compared with state-of-the-art methods under various conditions in real-time, achieving the highest speed of 33 fps.

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