路面裂缝图像自动识别算法综述

doi:10.3969/j.issn.1002-0268.2014.07.004

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摘要 Automatic pavement cracking detection is of great practical value for pavement maintenance, pavement performance evaluation and prediction, and material and structure design. However, detecting pavement cracks rapidly, precisely, completely, and robustly remains a challenge. Thus, literature review on automatic pavement crack detection was conducted, which included pre-processing methods aiming at image enhancement and de-noising, space-domain recognition algorithms based on thresholding, edge detection and seed growing, frequency-domain recognition algorithms, such as wavelet transform, and supervised learning methods. Shortcomings of these crack detection algorithms were summarized as follows: (1) illumination and oils tend to affect algorithm performance; (2) crack maps have poor continuity; (3) processing speed and recognition precision are not satisfying. Research prospects were also proposed as references to improve crack recognition algorithms, including (1) removing influences of texture and noises by combining boundary and area features, (2) designing optimization-based recognition algorithms that consider local and global features, and (3) detecting pavement cracks based on 3D images.

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	彭博
	蒋阳升
	蒲云

关键词 ： road engineering, automatic recognition algorithm, image processing, pavement cracks, image segmentation, edge detection, crack seeds, supervised learning

Abstract：Automatic pavement cracking detection is of great practical value for pavement maintenance, pavement performance evaluation and prediction, and material and structure design. However, detecting pavement cracks rapidly, precisely, completely, and robustly remains a challenge. Thus, literature review on automatic pavement crack detection was conducted, which included pre-processing methods aiming at image enhancement and de-noising, space-domain recognition algorithms based on thresholding, edge detection and seed growing, frequency-domain recognition algorithms, such as wavelet transform, and supervised learning methods. Shortcomings of these crack detection algorithms were summarized as follows: (1) illumination and oils tend to affect algorithm performance; (2) crack maps have poor continuity; (3) processing speed and recognition precision are not satisfying. Research prospects were also proposed as references to improve crack recognition algorithms, including (1) removing influences of texture and noises by combining boundary and area features, (2) designing optimization-based recognition algorithms that consider local and global features, and (3) detecting pavement cracks based on 3D images.

Key words： road engineering automatic recognition algorithm image processing pavement cracks image segmentation edge detection crack seeds supervised learning

收稿日期: 2014-10-28

基金资助:Supported by the National Natural Science Foundation of China(No.51108391);the Technological Innovation Projects of Fundamental Research Funds for the Central Universities (No.A0920502051208-99)

通讯作者: PENG Bo, E-mail:pengbo351@126.com E-mail: pengbo351@126.com

引用本文:

彭博, 蒋阳升, 蒲云. 路面裂缝图像自动识别算法综述[J]. Journal of Highway and Transportation Research and Development, 2015, 9(2): 13-20.
PENG Bo, JIANG Yang-sheng, PU Yun. Review on Automatic Pavement Crack Image Recognition Algorithms. Journal of Highway and Transportation Research and Development, 2015, 9(2): 13-20.

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