Centralized feature pyramid-based supervised deep learning for object detection model from GPR data

Abstract

To address low detection accuracy and speed due to the multisolvability of the ground-penetrating radar signal, we proposed a novel centralized feature pyramid-YOLOv6l–based model to enhance detection precision and speed in road damage and pipeline detection. The centralized feature pyramid was used to obtain rich intra-layer features and improve the network performance. Our proposed model achieves higher accuracy compared with the existing detection models. We also built two new evaluating indexes, relative average precision and relative mean average precision, to fully evaluate the detection accuracy. To verify the applicability of our model, we conducted a road field detection experiment on a ground-penetrating radar dataset we collected and found that the proposed model had good performance in increasing detection precision, achieving the highest mean average precision compared with YOLOv7, YOLOv5 and YOLOx models, with relative mean average precision and frame rate per second at 16.38% and 30.5%, respectively. The detection information for the road damage and pipeline were used to conduct three-dimensional imaging. Our model is suitable for object detection in ground-penetrating radar images, thereby providing technical support for road damage and underground pipeline detection.