{"title":"Efficient segmentation of water leakage in shield tunnel lining with convolutional neural network","authors":"Wenjun Wang, Chao Su, Guohui Han, Yijia Dong","doi":"10.1177/14759217231171696","DOIUrl":null,"url":null,"abstract":"Water leakage is a critical factor reflecting the structural safety of shield tunnels. Computer vision provides new opportunities to overcome the shortcomings of manual visual inspection and realize automatic detection of water leakage regions. In this study, we propose a leakage segmentation model with an encoder–decoder structure. The encoder adopts multi-branch convolutional attention for feature fusion, and the decoder adopts a lightweight design that only contains multi-layer perceptron. Standard convolution in multi-branch is decomposed to two depth-wise strip convolutions to realize lightweight design and extract strip-like features. Extensive ablation and comparative studies were conducted to test model performance. Test results show that our model achieves robust detection of water leakage under strong noise background, reaching an intersection over union of 90.75% with performance-computation trade-off. Consequently, the proposed method can be an effective alternative to the current visual inspection technologies, and provide a nearly automated inspection platform for shield tunnels.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2023-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Health Monitoring-An International Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/14759217231171696","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Water leakage is a critical factor reflecting the structural safety of shield tunnels. Computer vision provides new opportunities to overcome the shortcomings of manual visual inspection and realize automatic detection of water leakage regions. In this study, we propose a leakage segmentation model with an encoder–decoder structure. The encoder adopts multi-branch convolutional attention for feature fusion, and the decoder adopts a lightweight design that only contains multi-layer perceptron. Standard convolution in multi-branch is decomposed to two depth-wise strip convolutions to realize lightweight design and extract strip-like features. Extensive ablation and comparative studies were conducted to test model performance. Test results show that our model achieves robust detection of water leakage under strong noise background, reaching an intersection over union of 90.75% with performance-computation trade-off. Consequently, the proposed method can be an effective alternative to the current visual inspection technologies, and provide a nearly automated inspection platform for shield tunnels.
期刊介绍:
Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.