A Codec Road Detection Network with Edge Extraction and Multi-Resolution Information Supplement

With the development of deep learning, automated road detection has gradually become a popular topic in remote sensing image processing. However, existing road detection methods are still insufficient in occlusion problem, resulting in the occluded road being difficult to be detected and thus broken. In order to solve this problem, we propose a multi-resolution codec road detection network (MCRDnet). MCRDnet consists of a backbone with a codec structure, an edge extraction branch (EEB) and an information supplement module (ISM). The backbone is used to extract the features of the road. In addition, EEB is responsible for extracting edge information and interacting with the backbone to smooth the road edge regions. Moreover, ISM also adopts the codec structure but with the input of the images after 2-fold and 4-fold downsampling. The decoder of ISM plays the roles of image restoration and feature mapping at the same time, fusing features into the backbone to supplement the missing information affected by occlusion. The experimental results on the RNBD dataset demonstrate the effectiveness of the proposed method for mitigating the breakage phenomenon in road detection results.