Vehicle-Road Environment Perception Under Low-Visibility Condition Based on Polarization Features via Deep Learning

Visual perception system is the key component of safety driving assistance and unmanned driving systems, and the perceptive performance of which will directly affect the running safety. There are various perception algorithms for the visual system, especially in recent years, deep learning algorithms are adopted with increasing popularity among them. However, the majority of existing works mainly focuses on daytime scenes with favorable illumination and weather conditions, and relies on visible light with intensity imaging equipment. This paper is based on polarization features acquired by deep learning methods, attempting to address the problems about vehicle-road environment perception in Low-Visibility Condition. Polarization is one of the essential properties of matter, here we propose a new method by studying and analyzing the transmission characteristics of the target’s polarization information. In the proposed method, the polarization features of vehicle-road environment are first acquired by a kind of three-channel polarization imaging device, then, fused together with an intensity image, and finally followed by a semantic segmentation operation by deep networks. The experimental results clearly demonstrate that the effect of perception based on polarization features via deep learning are greatly improved compared with intensity imaging, especially in low illumination and severe weather conditions. It is of great value for environmental robustness of visual perception system and further traffic safety.