Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels

Abstract

Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is placed on trains to implement structural health monitoring in tunnels. Through the image processing technology proposed in this paper, water leakage areas in subway tunnels can be found and repaired in real time. A lightweight automatic segmentation approach to water leakage using hybrid-supervised-deep-learning technology is proposed. This approach consists of the weakly supervised learning Water Leakage-CAM and fully supervised learning WRDeepLabV3+. The Water Leakage-CAM is used for the automatic labeling of data. The WRDeepLabV3+ is used for the accurate identification of water leakage areas in subway tunnels. Compared with other end-to-end semantic segmentation networks, the hybrid-supervised learning approach can more completely segment the water leakage region when dealing with water leakage in complex environments. The hybrid-supervised-deep-learning approach proposed in this paper achieves the highest MIoU of 82.8% on the experimental dataset, which is 6.4% higher than the second. The efficiency is also 25% higher than the second and significantly outperforms other end-to-end deep learning approaches.

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

“…Siriborvornratanakul et al [93] Deep Learning X Sajedi et al [96] Deep Learning X X Meng et al [99] Deep Learning X Benkhoui et al [ Regarding deep learning approaches, most of the selected studies addressed only binary classification problems, including [88,89,92,93,99,100,102,105,110]. Only a few studies also addressed multi-class classification problems in addition to binary classification, such as [86] (type of damage: four classes), [96] (bridge components: seven classes), [87] (several multi-class subsets), [104] (type of damage: three classes), [107] (several multi-class subsets), and [113] (type of damage: five classes).…”

Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends

“…Zhang et al [50] introduced a displacement monitoring method based on Mask R-CNN, which achieves the purpose of displacement detection by extracting the mask information to obtain the coordinates of the calibrated object. Qiu et al [174] proposed that WRDeepLabV3+ combined with the class activation map could accurately identify leakage in subway tunnels, which could segment the leakage area more thoroughly.…”