One-stage 3D profile-based pavement crack detection and quantification
Zhihao Pan, Jinchao Guan, Xu Yang, Fan Kang, Jeremy C.H. Ong, Ningqun Guo, Xin Wang
Abstract
To improve the robustness and efficiency of pavement crack inspection tasks, a one-stage pavement crack detection and quantification method is proposed to directly extract crack geometry from 3D pavement profile. A low-cost stereo imaging system is implemented for high-resolution 3D reconstruction. Based on the generated 3D profile, an algorithm that integrates crack detection and quantification is developed to obtain the crack map, length, width, and depth simultaneously. The results show that the proposed method is robust to surrounding noises and has no demand for prior data. Compared with deep learning-based crack detection methods, the mean IoU of the proposed method is 6.23% higher than that of state-of-the-art methods. The proposed method outperforms existing methods in terms of crack length and width measurement. Moreover, the proposed method automatically measures crack depth with a correlation coefficient R2 of 0.9679 against the manual measurement.