GPR Imagery-Based Internal Defect Evaluation System for Railroad Tunnel Lining Using Real-Time Instance Segmentation

Railroad tunnel linings may have voids filled with water or air, or areas that lack compaction due to construction processes. These structural defects are hard to detect and require regular inspections to ensure safety. Existing convolutional neural network (CNN)-based inspection systems that employ ground-penetrating radar (GPR) imagery sometimes cannot fully assess the defects and ensure the structural integrity of the linings. This study develops an internal defect evaluation system based on an innovative computer vision (CV) architecture named You only look at tunnel coefficients (YolaTC) for monitoring railroad tunnel linings. First, YolaTC is equipped with a novel backbone that has multiple receptive fields available to strengthen multigranularity feature representation and enrich semantic information in the feature space. Then, a new strip pooling (SP) module is developed and incorporated to model long-range context dependencies, improving the segmentation of long but narrow defect regions. Second, the system integrates a novel skeleton extraction-based deterioration level evaluation (DLE) method that can calculate the actual defect length to quantify the level of void degradation. Finally, extensive comparative experiments conducted on a customized dataset established with GPR imagery demonstrate that the proposed system can effectively convert GPR images into valuable information with high accuracy, rapid processing speed, and good adaptation capability in complex tunnel structures.