Pixel-level tunnel crack segmentation using a weakly supervised annotation approach

Abstract

Automatic crack detection plays an essential role in ensuring the safe operation of tunnels, which is also challenging work in reality. In this paper, an innovative framework, which combines the weakly supervised learning methods (WSL) and the fully supervised learning methods (FSL), is presented to detect and segment the cracks in the tunnel images. Firstly, a WSL-based segmentation network Crack-CAM is proposed to annotate the collected data instead of using the traditional manual annotation process. By applying the proposed E-Res2Net101 structure and tuning some hyper-parameters, an FSL-based method named DeepLabv3+ is optimized to enhance the segmentation performance. After the crack segmentation, the risk levels of the detected cracks are judged using a new evaluation metric. In addition, the mean error of the lengths, the mean widths, and the areas are calculated for different types of cracks. A crack dataset in tunnel scenes that contain 3,921,726 sub-images that are cropped from 521 raw images is built to demonstrate the effectiveness of the presented methods. Based on the proposed dataset, the modified DeepLabv3+ achieves the highest MIoU of 0.786 and the best F1 of 0.865. Besides, the proposed framework combining WSL methods (automatic data annotation) and the FSL methods achieved a performance comparable to the framework that is based on manual annotation and the FSL methods, which demonstrates the WSL-based Crack-CAM can label images correctly.