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Deep learning-based automatic recognition of water leakage area in shield tunnel lining

机译：基于深入的学习的自动识别盾构隧道衬里的漏水区

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摘要

Difficulties in visual inspection of metro shield tunnels induced by various on-site installations (e.g., lighting instruments and pipelines) have been resolved by two-stage structural damage detection techniques based on deep learning known as the state-of-the-art on surface defects recognition. However, thresholds of the Intersection over Union (IoU) are set extremely low in two-stage segmentation models to obtain more positive samples to suppress overfitting during the training stage. Therefore, the pixel segmentation results generally contain lots of noise and are difficult to meet the requirements of engineering. Accordingly, this study adopted (1) data augmentation to increase the number of positive samples, (2) transfer learning to improve the robustness of convolutional layers, and (3) cascade strategy to enhance the quality of samples. The segmentation results of the improved model on the validation set demonstrated that the above methods achieved high precision pixel segmentation of water leakage (AP(0.5) = 0.806), which was greater than the classical deep learning model (i.e., Mask R-CNN with AP(0.5) = 0.530). Given the fact that the number of water leakage pixels cannot be regarded as reliable metrics to evaluate the security situation of the shield tunnel, a series of field experiments were conducted to obtain the calibration relationship between the number of target pixels and true areas. The average error rate of the fitted curve was also by only 2.59%, which is within the tolerance of the engineering. Consequently, the proposed method could automatically and accurately calculate the water leakage area from the dataset of images. All the water leakage images used in this study can be downloaded freely from https://doi.org/10.17632/xz2nykszbs.1.
机译：通过基于被称为最先进的表面的深度学习，通过两级结构损伤检测技术解决了各种现场安装（例如，照明仪器和管道）的地铁屏蔽隧道视觉检查困难缺陷识别。然而，在两级分割模型中，联盟（iou）交叉口的阈值非常低，以获得更多正样品以在训练阶段抑制过度拟合。因此，像素分割结果通常包含大量噪声，并且难以满足工程的要求。因此，本研究采用（1）数据增强增加了阳性样本的数量，（2）转移学习，提高卷积层的稳健性，（3）级联战略以提高样品的质量。验证组上改进模型的分割结果表明，上述方法实现了漏水的高精度像素分段（AP（0.5）= 0.806），这大于经典深度学习模型（即，带有掩模R-CNN的掩模R-CNN） AP（0.5）= 0.530）。鉴于漏水像素的数量不能被视为可靠的度量来评估屏蔽隧道的安全情况，进行了一系列现场实验，以获得目标像素数量和真实区域之间的校准关系。拟合曲线的平均误差率也仅为2.59％，这在工程的公差范围内。因此，所提出的方法可以自动和准确地从图像数据集计算漏水区域。本研究中使用的所有漏水图像可以从https://doi.org/10.17632/xz2nykszbs1自由下载。

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  • 来源
    《Tunnelling and underground space technology》 |2020年第10期|103524.1-103524.14|共14页
  • 作者

    Xue Yadong; Cai Xinyuan; Shadabfar Mahdi; Shao Hua; Zhang Sen;

  • 作者单位

    Tongji Univ Key Lab Geotech & Underground Engn Minister Educ Shanghai Peoples R China|Tongji Univ Dept Geotech Engn Shanghai Peoples R China;

    Tongji Univ Key Lab Geotech & Underground Engn Minister Educ Shanghai Peoples R China|Tongji Univ Dept Geotech Engn Shanghai Peoples R China;

    Tongji Univ Key Lab Geotech & Underground Engn Minister Educ Shanghai Peoples R China|Tongji Univ Dept Geotech Engn Shanghai Peoples R China;

    Shanghai Rail Transit Maintenance Support Co Ltd Shanghai Peoples R China;

    Tongji Univ Key Lab Geotech & Underground Engn Minister Educ Shanghai Peoples R China|Tongji Univ Dept Geotech Engn Shanghai Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Mask R-CNN; Data augmentation; Transfer learning; Cascade strategy; Water leakage area;

    机译：面具R-CNN;数据增强;转移学习;级联战略;漏水区;

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