Efficient segmentation of water leakage in shield tunnel lining with convolutional neural network

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Wenjun Wang, Chao Su, Guohui Han, Yijia Dong
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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.
用卷积神经网络有效分割盾构隧道衬砌漏水
漏水是反映盾构隧道结构安全性的一个关键因素。计算机视觉为克服人工视觉检测的缺点,实现漏水区域的自动检测提供了新的机会。在这项研究中,我们提出了一个具有编码器-解码器结构的泄漏分割模型。编码器采用多分支卷积注意力进行特征融合,解码器采用仅包含多层感知器的轻量级设计。将多分支中的标准卷积分解为两个深度条形卷积,以实现轻量级设计并提取条形特征。进行了广泛的消融和比较研究,以测试模型性能。测试结果表明,我们的模型在强噪声背景下实现了对漏水的鲁棒检测,在性能计算权衡的情况下达到了90.75%的交集。因此,所提出的方法可以作为当前视觉检测技术的有效替代方案,并为盾构隧道提供一个几乎自动化的检测平台。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
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.
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