Automatic recognition of tunnel lining elements from GPR images using deep convolutional networks with data augmentation

Abstract

Abstract Tunnel lining inspection using ground penetrating radar (GPR) is a routine procedure to ensure construction quality. Yet, the interpretation of GPR data relies heavily on manual experience that may lead to low efficiency and recognition error when a large volume of data is involved. We introduced a deep learning-based automatic recognition method to identify tunnel lining elements, including steel ribs, voids, and initial linings from GPR images. Based on the mask region-based convolutional neural network (Mask R-CNN), this approach uses the 101-layer deep residual network (ResNet101) with the feature pyramid network (FPN) to extract features, the region proposal network (RPN) to generate candidate regions, a group of fully connected layers to detect the presence and locations of steel ribs and voids, and a fully convolutional network (FCN) to segment the area of the initial lining. To improve the recognition performance of the network, the finite-difference time-domain (FDTD) method and deep convolutional generative adversarial network (DCGAN) are employed to create synthetic GPR images for data augmentation. The test results on a synthetic example show that the mean absolute errors for steel rib, void, and initial lining thickness recognition are 1.2, 2.2, and 4.2\xa0mm, respectively, demonstrating the feasibility of the recognition network. In a field GPR survey experiment, the recognition accuracies achieved 96.02%, 91.17%, and 95.45% for the three targets. With the optimal proportions of synthetic images added to the training dataset, the accuracies were further improved to 98.86%, 94.53%, and 99.27%, respectively.