Water Leakage and Crack Identification in Tunnels Based on Transfer-Learning and Convolutional Neural Networks

Abstract

In order to solve the problems of long artificial time consumption, the inability to standardize the degree of damage, and the difficulty of maintaining data in traditional tunnel disease detection methods, this paper proposes the use of Residual Network (ResNet) models for tunnel water leakage and crack detection. ResNet proposes a residual learning framework to ease the training of networks that are deeper than those previously used. Furthermore, ResNet explicitly reformulates the layers as learning the residual functions of the reference layer inputs, rather than learning the unreferenced functions. The ResNet model is built on the Tensorflow Deep Learning (DL) framework and transfer-learning is used to optimize the model. The ResNet-V1 can be obtained by pre-training in ImageNet. The fully connected layers of the ResNet-V1 were modified to four classifications of tunnel disease. Then, the SoftMax function is used to recognize the tunnel diseases. Four network structures have been chosen, i.e., ResNet34 and ResNet50, with and without Transfer-learning, respectively. Those models were selected for testing and training on the sample dataset, and these four network structures were compared and analyzed using five types of evaluation indicators, which are the confusion matrix, accuracy, precision, recall ratio and F1. In identifying tunnel cracks and water leakage, the accuracy of ResNet50 and ResNet34 using the transfer-learning were 96.30% and 91.29%, and the accuracy of ResNet50 was 5.01% higher than that of ResNet34; for the network structure without the transfer-learning, the accuracy of ResNet50 was 90.36% and ResNet34’s accuracy was 87.87%. These data show that the accuracy of ResNet50 is higher than that of ResNet34 with or without the transfer-learning, and the deep structure framework is superior in the identification of tunnel diseases; secondly, comparing the network structures with and without the transfer-learning, it can be found that using the Transfer-Learning can improve the ResNet network’s accuracy for tunnel disease identification. The experiments and reliability analysis demonstrate the intelligent tunnel disease identification method proposed in this paper, and its good robustness and generalization performance. This method can be used for the rapid identification of cracks and water leakage in a tunnel survey, construction and maintenance, which has practical engineering implications for tunnel disease detection.

Keywords

• tunnel diseases
• deep learning
• ResNet
• transfer-learning
• image augmentation