An End-to-End Traffic Visibility Regression Algorithm
Hongshuai Qin, Huibin Qin
Abstract
Traffic visibility detection plays a vital role in intelligent transportation, autonomous driving, safe driving, etc. Convolutional neural networks (CNNs) based regression and classification algorithms have been shown competitive performance in many applications, but little attention has been paid to traffic visibility identification. In this paper, we propose a trainable end-to-end system called traffic visibility regression network (TVRNet). TVRNet takes a road image as input and outputs its visibility value. TVRNet adopts CNNs based deep architecture, uses appropriate filters to extract fog density-related features, and exploits the parallel convolution for multi-scale mapping. Later, a new type of non-linear activation function called Modified_sigmoid function is used. We synthesize labeled visibility datasets comprised of multi-scene and single-scene based on the actual road sense to train the visibility regression network. Extensive experiments and comparisons with other popular algorithms are performed to verify our method in road visibility estimation.