Automatic defect detection in operational high-speed railway tunnels guided by train-mounted ground penetrating radar data

Abstract

The tunnel diseases of high-speed railways present significant risks under the substantial dynamic loads from trains. Train-mounted ground-penetrating radar detection data for tunnels relies heavily on manual judgment, resulting in low efficiency and inaccurate identification. Meanwhile, automatic detection models need more samples. We propose an automated recognition framework, guided by generated data, to address these issues for identifying defects in operational high-speed railway tunnels. In this work, we propose a multi-category GPR generative adversarial network (MGPR-GAN) to generate many defect data across different categories to guide YOLOv5 training instead of relying predominantly on real data. It then uses generated data (65.43%) alongside a smaller proportion of real defect data (34.57%) to guide a YOLOv5 model. The model outputs the defect category, confidence level, and defect width and height estimates in conjunction with prior information. We validated our method using samples from 71 tunnels. The results reveal that our proposed method outperforms the average accuracy of the three advanced identification methods (FasterRCNN, SSD, and YOLOv5) by over 20.62%.