GPR-RCNN: An Algorithm of Subsurface Defect Detection for Airport Runway Based on GPR

Abstract

Detection of subsurface defects is important for maintaining runway structural health and reliability. A potential solution is to employ a robot equipped with a Ground Penetrating Radar (GPR) to perform subsurface scanning. To automate the inspection process, we develop a subsurface defect detection algorithm which is a deep learning algorithm that fuses 2D planar features in each panel in GPR B-scans and 3D voxel-wise features in GPR C-scan to robustly detect regions with defects even in the presence of significant noises. Named as GPR-RCNN, we have tested our algorithm with real airport runway data collected from three international airports using our runway inspection robot. The experimental results show that our proposed GPR-RCNN achieves superior results when comparing to state-of-the-art techniques. Specifically, our method achieves F1-measures at 62%, 33%, 81%, and 87% for void, crack, subsidence and pipe, respectively.