Recognition of void defects in airport runways using ground-penetrating radar and shallow CNN

The interpretation of ground-penetrating radar (GPR) data usually depends on manual work, thus yielding unsatisfactory accuracy when processing complicated cases. To address this issue, the finite-difference time-domain method was adopted to explore the void-related features in GPR images, and guide the field survey to locate the void area. On this basis, a dataset including 811 void features was established from 10 airport runways using an 800 MHz ground-coupled GPR system. Data augmentation was used to enlarge the dataset and train four configurations of a mixed convolution neural network (CNN) model with feature extractors (ResNet18 and ResNet50) and object detectors (YOLOv2 and Faster R-CNN). To make the detection process automatic, an incremental random sampling (IRS) approach was used to generate images from GPR data and then fed into the trained model. The experimental results demonstrated that the shallow ResNet18-YOLOv2 with the IRS method is a promising strategy for void detection in airport runways.