论文标题
使用卷积神经网络和热图回归的非结构化道路消失点检测
Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression
论文作者
论文摘要
非结构化的道路消失点（VP）检测是一个具有挑战性的问题，尤其是在自动驾驶领域。在本文中，我们提出了一种新的解决方案，该解决方案结合了卷积神经网络（CNN）和热图回归，以检测非结构化的道路VP。拟议的算法首先采用轻质的骨干，即深度卷积修改的HRNET，以提取非结构化的道路图像的层次特征。然后，利用三种高级策略，即多尺度监督学习，热图超分辨率和坐标回归技术来实现快速，高精度的非结构化道路VP检测。 Kong数据集的经验结果表明，我们提出的方法与在各种条件下实时的最新方法相比具有最高的检测准确性，达到了33 fps的最高速度。
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.
