LAUSR

A long-wave infrared polarization imaging technique recently has been applied in full-time road detection. However, the existing heuristic method has the limitation of fully using the polarization information of the road. In this Letter, we propose a polarization-guided road detection network collaborating with the distinguishable polarization characteristics of the road. A two-branch network is proposed to perform accurate road detection with infrared polarization images as inputs. A coarse road map obtained by thresholding the polarization images of the road guides the network to focus on the road regions through a polarization-guided branch. We also design a road-region-aware feature fusion module to fuse the features from two branches. This customized design of the network gives full play to the advantages of deep learning networks and polarization information. Experiments on a public infrared polarization dataset of road scenes demonstrate that the proposed road detection network outperforms state-of-the-art real-time segmentation networks with fewer parameters and faster speed.