Deep learning with visual explanations for leakage defect segmentation of metro shield tunnel

Tunnel lining leakage is a crucial indicator of metro shield tunnels’ safety status. For automatic, rapid and accurate detection of leakages, this paper proposes a deep learning-based approach with enhanced efficiency, accuracy and interpretability. First, a total of forty U-shaped semantic segmentation models are developed by coupling UNet and UNet++ with six types of classification convolutional neural networks. Then, multiple evaluation indices, i.e., accuracy, computational complexity, and model complexity are introduced to determine the optimal leakage detection models which achieve a balance between efficiency and accuracy. Finally, Gradient-weighted Class Activation Mapping Plus Plus (Grad-CAM++) is leveraged to understand the mechanisms behind the ‘black-box’ of deep learning-based models. The experimental results show that UNet with encoder EfficientNetB6 is the most applicable model amongst UNet-based models, which gets 84.97 % intersection over union (IoU), requires 115.77 billion floating-point operations (FLOPs), and has 42.49 million parameters. UNet++ with encoder EfficientNetB5 achieves 87.70 % IoU, needs 327.26 billion FLOPs, and has only 30.86 million parameters, thereby becoming the best-performing model among UNet++-based models. The quantitative comparison with existing dominant approaches also proves that our proposed models have high accuracy and low time and space complexities. Furthermore, based on the visual explanations, it concludes that deepening and widening the encoder networks can boost models’ performance, and the two feature fusion methods, namely simple skip connection and dense skip connections, play a crucial role in achieving precise tunnel leakage segmentation. The developed algorithms of this study provide a pixel-wise segmentation basis for fast and accurate quantitative assessment of metro tunnel health.