@article{ebabb40a9fb64efea07cf33231d1c8ef,
title = "Automated pixel-level pavement distress detection based on stereo vision and deep learning",
abstract = "Automated pavement distress detection based on 2D images is facing various challenges. To efficiently complete the crack and pothole segmentation in a practical environment, an automated pixel-level pavement distress detection framework integrating stereo vision and deep learning is developed in this study. Based on the multi-view stereo imaging system, multi-feature pavement image datasets containing color images, depth images and color-depth overlapped images are established, providing a new perspective for deep learning. To alleviate computational burden, a modified U-net deep learning architecture introducing depthwise separable convolution is proposed for crack and pothole segmentation. These methods are tested in asphalt roads with different circumstances. The results show that the 3D pavement image achieves millimeter-level accuracy. The enhanced 3D crack segmentation model outperforms other models in terms of segmentation accuracy and inference speed. After obtaining the high-resolution pothole segmentation map, the automated pothole volume measurement is realized with high accuracy.",
keywords = "Crack and pothole segmentation, Deep learning, Depthwise separable convolution, Pavement distress detection, Stereo vision, U-net",
author = "Jinchao Guan and Xu Yang and Ling Ding and Xiaoyun Cheng and Lee, {Vincent C.S.} and Can Jin",
note = "Funding Information: The study presented in the article was partially supported by the National Natural Science Foundation of China (No. 52078049), Fundamental Research Funds for the Central Universities, CHD (No. 300102210302, No. 300102210118) and the 111 Project of Sustainable Transportation for Urban Agglomeration in Western China (No. B20035). The authors would like to thank the editor and the anonymous reviewers for their constructive comments and valuable suggestions to improve the quality of the article. The authors also appreciate the help from Prof. Hyoungkwan Kim from Yonsei University, Korea, and Ph.D. student Jingwei Liu from Monash University, Australia. Funding Information: The study presented in the article was partially supported by the National Natural Science Foundation of China (No. 52078049 ), Fundamental Research Funds for the Central Universities, CHD (No. 300102210302 , No. 300102210118 ) and the 111 Project of Sustainable Transportation for Urban Agglomeration in Western China (No. B20035 ). The authors would like to thank the editor and the anonymous reviewers for their constructive comments and valuable suggestions to improve the quality of the article. The authors also appreciate the help from Prof. Hyoungkwan Kim from Yonsei University, Korea, and Ph.D. student Jingwei Liu from Monash University, Australia. Publisher Copyright: {\textcopyright} 2021 Elsevier B.V. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = sep,
doi = "10.1016/j.autcon.2021.103788",
language = "English",
volume = "129",
journal = "Automation in Construction",
issn = "0926-5805",
publisher = "Elsevier BV",
}