Automated pavement distress segmentation on asphalt surfaces using a deep learning network

Recently, many deep learning methods have achieved great results in the field of automated pavement distress detection, but most of them ignore other types of distresses beyond cracks. This paper proposes an efficient deep learning framework for automated asphalt pavement distress segmentation called pavement distress segmentation network (PDSNet). PDSNet can effectively segment multiple asphalt pavement distresses, including crack, pothole, raveling, patch, and sealed crack. It consists of two parallel feature extraction branches. One is the P branch to extract prior global information. The other is the U branch to obtain local information. By utilising the global and local features together, PDSNet can produce precise segmentation results under complicated circumstances. For deep learning purposes, a pavement distress dataset consisting of 4000 pavement images is collected and manually labelled at pixel level. Each image of the pavement dataset is a two-channel image, which is concatenated by a 2D pavement image and the correspondingly 3D pavement image. Particularly, it is the first pavement distress dataset that utilises two-channel pavement images. According to the experimental results, PDSNet yields a performance with a MIoU of 83.7%. Compared with the state-of-the-art networks, PDSNet achieves the best MIoU and has considerable parameter number and inference time.