Automatic recognition of hidden defects behind railway tunnel lining using ground penetrating radar and deep learning

As a recognized non-destructive testing method, ground penetrating radar (GPR) has been widely applied for hidden defect detection of railway tunnels. However, the manual interpretation of GPR data is time-consuming and has a high demand of the operator’s experience. This paper presents an automatic recognition algorithm, which is based on a faster region-based convolutional neural network (Faster R-CNN), to recognize the hidden defects behind the railway tunnel lining, including non-compactness, voids with air- or water-filled, and cracks. To improve the recognition ability of the proposed algorithm, transfer learning is used by pre-training the network based on two groups of GPR dataset. Field experiment results preliminarily show that the proposed recognition algorithm can accurately identify and classify the hidden defects in the field GPR data.