A novel method of void detection in rebar-affected areas based on transfer learning and improved YOLOv8

Abstract

The rebar mesh inside the tunnel lining introduces significant interference in detecting defects, reducing their visibility in GPR images. This study proposes a global-to-local secondary recognition method based on an improved YOLOv8 model to address this challenge. Two datasets—global and local GPR images—were used, with an attention mechanism integrated into the YOLOv8 architecture. The improved YOLOv8 structure has been shown to increase the mean Average Precision (mAP) by 9.36 % and 3.86 % for the two datasets, respectively. Optimal performance was achieved with a rebar spacing of 0.4 m and a secondary recognition confidence of 0.867, while a rebar-defect distance of 1.20 m reached a confidence of 0.858. The model accurately identified the void defect shapes. Compared to traditional rebar signal suppression methods, this approach simplifies data processing, enhances accuracy, and reduces training costs. A tunnel field case further validated the method, boosting GPR image recognition confidence from 0.37 to 0.73, significantly improving the automated detection of tunnel lining defects.