Automatic characterization of pavement crack length and width using a dual-stage hybrid framework

Abstract

Accurate segmentation and quantitative characterization of pavement cracks are critical for road condition assessment and preventive maintenance. However, existing methods often lack robustness under complex field conditions, leading to inaccurate estimation of crack length and width. In this study, a two-step method is proposed for automatic crack segmentation and characterization using a vehicle-mounted system. Firstly, a high-resolution pavement dataset comprising 10,348 images, augmented with diverse environmental conditions, is established for model training and evaluation. Secondly, an improved SegFormer network with coordinate attention is trained and employed to enhance crack boundary preservation and suppress background noise in segmentation. Thirdly, an improved A* algorithm integrated with a dynamic window approach (DWA) is applied to extract continuous crack centerlines and adaptively compute length and width through perpendicular distance measurements. Experimental results demonstrate that the proposed method achieves superior performance with an accuracy of 98.74%, mPA of 85.79%, and inference speed of 149 frames per second, outperforming traditional segmentation model. Field validation further confirms that the relative error of crack length and width estimation is lower than 10%. These findings indicate that the proposed two-step method provides an accurate, efficient, and robust solution for real-time pavement crack characterization in practical road inspection scenarios.