A GPR Imagery-Based Real-Time Algorithm for Tunnel Lining Void Identification Using Improved YOLOv8

Tunnel lining voids, a common latent defect induced by the coupling effects of complex geological, environmental, and load factors, pose severe threats to operational and personnel safety. Traditional detection methods relying on Ground-Penetrating Radar (GPR) combined with manual interpretation suffer from high subjectivity, low efficiency, frequent missed or false detections, and an inability to achieve real-time monitoring. Thus, this paper proposes an intelligent identification methodology for tunnel lining voids based on an improved version of YOLOv8. Key enhancements include integrating the RepVGGBlock module, dynamic upsampling, and a spatial context-aware module to address challenges from diverse void geometries—resulting from interactions between the environment, geology, and load—and complex GPR signals caused by heterogeneous underground media and the varying electromagnetic properties of materials, which obscure void–background boundaries, as well as interference signals from detection processes. Additionally, the C2f-Faster module reduces the computational complexity (GFLOPs), parameter count, and model size, facilitating edge deployment at detection sites to achieve real-time GPR signal interpretation for tunnel linings. Experimental results on a heavy-haul railway tunnel’s lining defect dataset show 11.57% lower GFLOPs, 14.55% fewer parameters, and 13.85% smaller weight files, with average accuracies of 94.1% and 94.4% in defect recognition and segmentation, respectively, meeting requirements for the real-time online detection of tunnel linings. Notably, the proposed model is specifically tailored for void identification and cannot handle other prevalent tunnel lining defects, which restricts its application in comprehensive tunnel health monitoring scenarios where multiple defects often coexist to threaten structural safety.