Semantic Segmentation Algorithm for Night Traffic Scene Based on Visible and Infrared Images

At present, there are many semantic segmentation algorithms with excellent performance for intelligent driving vehicles, but most of them are only work well on scenes with good illumination. In order to solve the problem of scene segmentation under low illumination, this paper proposes a novel semantic segmentation algorithm which combines visible and infrared images. In this algorithm, two parallel encoders are designed as the input of the image, and the decoder divides the fused image output from the encoder. The model is based on ResNet algorithm, and the residual attention module is used in each branch to mine and enhance the spatial features of multilevel channels to extract image information. Experiments are carried out on publicly available thermal infrared and visible data sets. The results show that the algorithm proposed in this paper is superior to the algorithm using only visible images in semantic segmentation of traffic environment.