基于三维光影模型的公路路面裂缝自动识别算法

阳恩慧; 张傲南; 丁世海; 王郴平

doi:10.3969/j.issn.0258-2724.2017.02.011

基于三维光影模型的公路路面裂缝自动识别算法

doi: 10.3969/j.issn.0258-2724.2017.02.011

阳恩慧^1,2,
张傲南^1,2,
丁世海^1,2,
王郴平^1,2

基金项目:

国家自然科学基金资助项目(51308477，U1534203)

中央高校基本科研业务费专项资金资助项目(2682015CX091)

详细信息

作者简介:
阳恩慧(1982-),男,副教授,博士,研究方向为路面微观材料性能与伤损识别,E-mail: ehyang@home.swjtu.edu.cn

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出版历程
- 收稿日期: 2016-04-21
- 刊出日期: 2017-04-25

Automatic Detection Method for Highway Pavement Cracking Based on the 3D Shadow Modeling

摘要

摘要: 针对公路路面裂缝的自动化三维图像识别技术的研究热点问题，为有效提高裂缝识别算法的准确率与可靠性，基于三维图像提出利用三维光影模型(3D Shadow Modeling)来实现公路路面表面裂缝的自动识别新方法。该方法利用裂缝区高度低于周边高度的特性，通过三维光影模型将裂缝投影为阴影区，继而通过对阴影的形态分析来识别裂缝，并采用连通域分析与线性形态分析方法，消除图像噪声。研究结果表明，采用本文提出的裂缝自动识别算法可以达到92.93%的路面裂缝自动识别准确率。
- 三维光影模型 /
- 公路路面 /
- 裂缝 /
- 识别算法
Abstract: Automatic detection method for pavement cracking based on 3D image technology has been a hot topic. In order to improve the accuracy and reliability of the cracking detection, a new method based on the 3D shadow modeling was proposed, which utilizes that the height of the fracture zone is lower than the surrounding area, and the shadow model was used to identify the cracking by the virtual light projection and then, the linear shape analysis method was used to discriminate cracks detection. Furthermore, both the connected-component analysis method and linear shape analysis method were adopted to remove the image noises. The results show that the automatic detection method can achieve 92.93% of accuracy in pavement cracking detection.
- 3D shadow modeling /
- highway pavement /
- cracking /
- detection method

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参考文献(17)

KELVIN C P W, ROBERT P E. Investigation of image archiving for pavement surface distress surface distress survey[R]. Fayetteville: Mack-Blackwell Transportation Center, University of Arkansas, 1999.

OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man and Cybernetics, 1979, 9(1): 62-66.

CANNY J. A computational approach to edge detection[J]. Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698.

彭博,蒋阳升,韩世凡,等. 路面裂缝图像自动识别算法综述[J]. 公路交通科技,2014,31(7): 19-25. PENG Bo, JIANG Yangsheng, HAN Shifan, et al. A review of automatic pavement crack image recognition algorithms[J]. Journal of Highway and Transportation Research and Development, 2014, 31(7): 19-25.

张娟,沙爱民,高怀钢, 等. 基于数字图像处理的路面裂缝自动识别与评价系统[J]. 长安大学学报:自然科学版,2004,24(2): 18-22. ZHANG Juan, SHA Aimin, GAO Huaigang, et al. Automatic pavement crack recognition and evaluation system based on digital image processing[J]. Journal of Chang'an University: Natural Science Edition, 2004, 24(2): 18-22.

王华,朱宁,王祁. 应用计盒维数方法的路面裂缝图像分割[J]. 哈尔滨工业大学学报,2007,39(1): 142-144. WANG Hua, ZHU Ning, WANG Qi. Segmentation of pavement cracks using differential box-counting spproach[J]. Journal of Harbin Institute of Technology, 2007, 39(1): 142-144.

MARTIN D R, FOWLKES C C, MALIK J. Learning to detect natural image boundaries using local brightness, color, and texture cues[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5): 530-549.

STAHL J S, WANG S. Edge grouping combining boundary and region information[J]. IEEE Transactions on Image Processing, 2007, 16(10): 2590-2606.

SHU Zhibiao, GUO Yanqing. Algorithm on contourlet domain in detection of road cracks for pavement images[J]. Journal of Algorithms Computational Technology, 2013, 7(1): 15-26.

SAART,TALVIK O. Automatic asphalt pavement crack detection and classification using neural networks[C]//Proceedings of the 12th Biennial Baltic Electronics Conference.Tallinn: IEEE, 2010: 345-348.

KELVIN C P, WANG J. Elements of automated survey of pavements and a 3D methodology[J]. Journal of Modern Transportation, 2011(19): 51-57.

WANG K C P, LI Q, GONG W. Wavelet-based pavement distress image edge detection with trous algorithm[R]. Washington D C: TRB, National Research Council, 2007.

彭博,WANG K C P,陈成,等. 基于各向异性测度的路面三维图像裂缝识别[J]. 西南交通大学学报,2014,49(5): 888-895. PENG Bo, WANG K C P, CHEN Cheng, et al. 3D pavement crack Image detection based on anisotropy measure[J]. Journal of Southwest Jiaotong University, 2014, 49(5): 888-895.

GRAMA A, GUPTA A, KARYPIS G, et al. Introduction to parallel computing[M]. 2nd ed. Boston: Addison Wesley, 2003: 209-210.

DAVIS J, GOADRICH M. The relationship between precision-recall and ROC curves[C]//Proceeding of the 23rd International Conference on Machine Learning. Pittsburgh: ACM, 2006: 233-240.

FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8): 861-874.

DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.(中文编辑:唐晴英文编辑:周尧)

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文章访问数: 592
HTML全文浏览量: 89
PDF下载量: 276
被引次数: 0

基于三维光影模型的公路路面裂缝自动识别算法

doi: 10.3969/j.issn.0258-2724.2017.02.011

作者简介: 阳恩慧(1982-),男,副教授,博士,研究方向为路面微观材料性能与伤损识别,E-mail: ehyang@home.swjtu.edu.cn

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Automatic Detection Method for Highway Pavement Cracking Based on the 3D Shadow Modeling

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目录

作者简介:
阳恩慧(1982-),男,副教授,博士,研究方向为路面微观材料性能与伤损识别,E-mail: ehyang@home.swjtu.edu.cn