Development of an Open GPR Dataset for Enhanced Bridge Deck Inspection

Abstract

Bridge infrastructure in the United States is aging, necessitating efficient and accurate inspection methods. Ground-penetrating radar (GPR) is a widely used non-destructive testing (NDT) method for detecting subsurface anomalies in bridge decks. However, manual interpretation of GPR scans is labor-intensive, and annotated datasets for deep learning applications are limited. This study investigates YOLO-based deep learning models for automated rebar detection using a combination of real and synthetic GPR data. A dataset comprising 2255 real GPR images from four bridges and 20,000 simulated GPR scans was used to train and evaluate YOLOv8, YOLOv9, YOLOv10, and YOLOv11 under different training strategies. The results show that pretraining on simulated GPR data improves detection accuracy compared to conventional COCO pretraining, demonstrating the effectiveness of domain-specific transfer learning. These findings highlight the potential of simulated GPR data for training deep learning models, reducing reliance on extensive real-world annotations. This study contributes to AI-driven infrastructure monitoring, supporting the development of more scalable and automated GPR-based bridge inspections.