Real-Time Detection of Voids in Asphalt Pavement Based on Swin-Transformer-Improved YOLOv5

Abstract

Detecting buried objects using ground-penetrating radar (GPR) profiles typically requires manual interaction and considerable time. To resolve this problem, first, this research used gprMax to model the void defect more realistically and study its GPR image features. The team employed 3D radar to collect 2400 data points of voids from the test road and expressway and used data augmentation to augment the data. Then, convolutional neural network (CNN) algorithms such as Faster R-CNN, RetinaNet, YOLOv3, and YOLOv5, are improved based on the Swin Transformer. In this study, the Swin_YOLOv5 model performed the best with an F1 score of 98.5%, a recall rate of 98.4%, and a precision of 98.7%, and correctly recognized voids while the other three models had false positives (FP). Finally, this research group used a combination of Multiple Screen Shots(MSS) and Swin_YOLOv5_to recognise asphalt pavement voids in real time and achieved an accuracy rate of 89.5% in a field survey to achieve the purpose of real-time detection and identification of voids by GPR.