Two-stage convolutional neural network for road crack detection and segmentation

Automatic detection of road cracks is an important task to support road inspection for transport infrastructure. Various methods have been proposed for road crack detection and segmentation, however, there is no established method for handling real road images that are noisy and of low quality. In this paper, a new method utilising a two-stage convolutional neural network (CNN) is proposed for road crack detection and segmentation in images at the pixel level. Our novel contribution is a framework where the first stage serves to remove noise or artifacts and isolate the potential cracks to a small area, and the second stage is able to learn the context of cracks in the detected area. This is hence more effective than learning over the entire original noisy image. Extensive experiments on real datasets including public sources and our collected dataset have been conducted. The experimental results show that the two-stage CNN model outperformed existing approaches, especially for noisy, low-resolution images, and imbalanced datasets. Our approach achieves an F1-measure of over 0.91 on three datasets.

Automated summary

This paper proposes a new method utilizing a two-stage convolutional neural network for road crack detection and segmentation, which outperforms existing approaches in handling noisy, low-resolution images, and imbalanced datasets with an F1-measure of over 0.91 on three datasets.