Automated Identification of Pavement Structural Distress Using State-of-the-Art Object Detection Models and Nondestructive Testing
Xiaogang Guo, Ning Wang
Abstract
The rapid identification of structural damage in pavements remains a challenging issue. This paper proposes an automatic identification method that combines nondestructive testing, the ground penetrating radar (GPR), and object detection (OD) models for pavement structural assessment. Radar wave image data were collected from a 40 km single-lane track road and processed to create the training data set of 2,000 labeled images with two types of structural distress. Four OD models, including Faster R-CNN, YOLOv5, YOLOv8, and DETR, were adapted and trained on the data set to recognize structural damage in the images, and their prediction performances were compared. The results demonstrated that YOLOv8 was the state-of-the-art (SOTA) model, which achieved precise identification of the location and type of pavement structural damage with a mean average precision (mAP) of 98.5%. This study contributes to the improvement of latest OD models in the non-destructive inspection, facilitating the development of real-time automated pavement assessment and maintenance decision systems.