Three-dimensional ground-penetrating radar-based feature point tensor voting for semi-rigid base asphalt pavement crack detection

Three-dimensional Ground penetrating radar (3D-GPR) has been widely applied in nondestructive testing of concealed cracks within asphalt pavement. However, due to the weak GPR echo characteristics of concealed cracks and their susceptibility to environmental noise, automatic recognition of crack echo features has always faced significant challenges. To address this issue, numerous semi-rigid base crack images were collected and extracted using feature point tensor voting with 3D-GPR's efficient, non-destructive road structure detection. In this paper, the radar image is gridded by the ECA-ResNet network, and the center point of the detected crack grid is used as the feature point, and the continuous path of the crack is reconstructed by the tensor voting algorithm. The results show that this method achieves 90% crack extraction, which is superior to traditional target detection networks such as YOLOv5 and Fast R-CNN, providing an effective tool for rapid non-destructive detection of pavement cracks.