Evaluating the optimised YOLO-based defect detection method for subsurface diagnosis with ground penetrating radar

Abstract

Ground-penetrating radar (GPR) is an efficient and effective non-destructive method for diagnosing urban roads, but its interpretation requires complex analysis. To address this, we proposed an optimized YOLO-based framework for timely identification of road defects using GPR. We used transfer learning and data augmentation to optimize the framework and evaluated their effects. We compared six YOLO versions and found that YOLOv5_s performed best with less weight. The optimized YOLOv5_s-based framework was proposed to identify voids and separation between road layers using more than 100 km of real GPR data. We validated the framework by comparing it with professional visual interpretation and observed that it provided comparable accuracy within seconds. The study benchmarks YOLOv5_s for timely road inspection with GPR.