Tunnel Lining Recognition and Thickness Estimation via Optical Image to Radar Image Transfer Learning

The secondary lining of a tunnel is a critical load-bearing component, whose stability and structural integrity are essential for ensuring the overall safety of the tunnel. However, identifying lining structures and estimating their thickness using ground-penetrating radar (GPR) remain challenging due to several inherent limitations. First, the limited electromagnetic contrast between the primary and secondary linings results in weak interface reflections in GPR imagery, thereby hindering accurate delineation. Second, construction errors such as over-excavation or under-excavation often lead to complex interface geometries, further complicating the interpretation of GPR signals. To address these challenges, we propose an enhanced YOLOv8-seg network capable of performing pixel-level segmentation on GPR images to accurately delineate secondary lining regions and estimate their thickness. The model integrates a convolutional block attention module (CBAM) to refine feature extraction by emphasizing critical characteristics of the two interface layers through channel-wise and spatial attention mechanisms. The model is first pretrained on the COCO dataset and subsequently fine-tuned via transfer learning using a hybrid GPR dataset comprising real-world measurements and numerically simulated data based on forward modeling. Finally, the model is validated on real-world GPR measurements acquired from the Longhai tunnel. Experimental results demonstrate that the proposed method reliably identifies secondary tunnel linings and accurately estimates their average thickness.