CurSeg: A pavement crack detector based on a deep hierarchical feature learning segmentation framework

Abstract Automatic crack detection is challenging due to the poor continuity of cracks, the different widths of cracks, and the low contrast between cracks and the surrounding pavement. In this paper, a deep convolutional neural network called CurSeg is proposed, which achieves pixelwise segmentation of cracks in an end‐to‐end manner. In this approach, features at different scales are fused together to attain the context information from the cracks. The elaborately designed model can effectively suppress the propagation of noise and further refine the crack features by aggregating multiscale and multilevel features from low‐level to high‐level. Residual detail attention (RDA) is also introduced to better capture the line structure and the ability to accurately locate the crack position in a complex context to make the network more discriminative and robust. CurSeg is evaluated on four datasets to validate the effectiveness of the approach. The experimental results demonstrate that this method achieves state‐of‐the‐art performance on the four challenging datasets.