Paper Info
Title
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. (c) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.compind.2021.103545
COMPUTERS IN INDUSTRY
Keywords
DocType
Volume
crack segmentation, deep learning, weakly supervised learning, fully supervised learning, tunnel crack
Journal
133
ISSN
Citations 
PageRank 
0166-3615
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Hanxiang Wang100.68
Yanfen Li212.40
L. Minh Dang312.06
Sujin Lee401.01
Hyeonjoon Moon500.34