|Table of Contents|

Research progress on automatic image processing technology for pavement distress(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2019年01期
Page:
172-190
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Research progress on automatic image processing technology for pavement distress
Author(s):
XU Zhi-gang1 CHE Yan-li2 LI Jin-long1 ZHAO Xiang-mo1 PAN Yong13 WANG Zhong-ren4 WEI Na1 SONG Hong-xun1
(1. School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; 2. Shijiazhuang Huayan Transportation Technology Co., Ltd., Xi'an Branch, Xi'an 710000, Shaanxi, China; 3. Zhongzi Data Co., Ltd., Beijing 100083, China; 4. Division of Maintenance, California Department of Transportation, Sacramento 95835, California, USA)
Keywords:
pavement maintenance pavement distress image processing distress recognition crack detection distress classification
PACS:
U418.6
DOI:
-
Abstract:
The important research achievements on the automatic image processing technology for pavement distress were summarized. The research progress of key technologies in this field was analyzed, including the pavement distress detection system, image processing algorithm and evaluation of recognition algorithm. The detection accuracy and applicability were compared for the different pavement distress detection systems and target automatic recognition algorithms. The possible future research directions of automatic pavement distress image processing technology were presented. Research result shows that in the aspect of pavement distress detection system, from early image acquisition based on the photography technology to the current 3D laser scanning technology, the pavement image acquisition technology becomes more and more convenient and effective. However, there still exist some challenges in the automatic analysis on distress images and automatic recognition algorithm on targets. In the aspect of pavement distress image processing algorithm, the traditional algorithms of segmenting pavement distress targets evolve from the methods using single feature(such as grayscale and edge shape)to multi-feature fusion-based methods and graph optimization-based detection methods. Furthermore, there emerges some dedicated algorithms for recovering or connecting cracks, greatly improving the detection accuracy of crack recognition. Nonetheless, as the complexity of these algorithms grows up, the required computational resources and the size of prior knowledge base both sharply increase. In the aspect of evaluation and comparison of crack processing algorithms, manual segmentation is mainly used to evaluate automatic recognition results. At present, it is urgent to establish a large-scale pavement distress image database opening to the world, so as to objectively and effectively evaluate various existing image processing algorithms for pavement distress. Automatic image processing algorithms for pavement distress based on 2D image features analysis is difficult to achieve the best results with detection accuracy, algorithm versatility and real-time performance simultaneously. In recent years, a large number of scholars begin to use the deep learning neural network to automatically recognize pavement distress, but the technology is still in an active evolution process. In the aspect of improving the accuracy and efficiency of automatic recognition for pavement distress, the 3D laser scanning technology and the deep learning technology based on artificial intelligence will greatly promote the final breakthrough on automatic image recognition technology for pavement distress in the future. 1 tab, 20 figs, 93 refs.

References:


[1] 交通运输部.《2017年全国收费公路统计公报》解读[N].中国交通报,2018-08-24(2).
Ministry of Transport. Interpretation of “2017 National Toll Road Statistical Bulletin”[N]. China Communications News, 2018-08-24(2).(in Chinese)
[2] WANG K W. Highway data collection and information
management[C]∥TRB.Proceedings of the 3rd International Conference on Road and Airfield Pavement Technology. Washington DC: TRB, 1998: 1144-1152.
[3] WANG K C P. Designs and implementations of automated systems for pavement surface distress survey[J]. Journal of Infrastructure Systems, 2000, 6(1): 24-32.
[4] CAFISO S, DI GRAZIANO A, BATTIATO S. Evaluation of pavement surface distress using digital image collection and analysis[C]∥Yildiz Technical University. Seventh International Congress on Advances in Civil Engineering. Istanbul: Yildiz Technical University, 2006: 11-13.
[5] 马 建,赵祥模,贺拴海,等.路面检测技术综述[J].交通运输工程学报,2017,17(5):121-137.
MA Jian, ZHAO Xiang-mo, HE Shuan-hai, et al. Review of pavement detection technology[J]. Journal of Traffic and Transportation Engineering, 2017, 17(5): 121-137.(in Chinese)
[6] WANG K C P, GONG W G. Real-time automated survey system of pavement cracking in parallel environment[J]. Journal of Infrastructure Systems, 2014, 11(3): 154-164.
[7] HUANG Ya-xiong, XU Bu-gao. Automatic inspection of pavement cracking distress[J]. Journal of Electronic Imaging, 2006, 15(1): 185-188.
[8] CHENG H D, MIYOJIM M. Automatic pavement distress detection system[J]. Information Sciences, 1998, 108(1-4): 219-240.
[9] 啜二勇.国内路面自动检测系统研究历程及展望[J].中国高新技术企业,2009(19):195-196.
CHUO Er-yong. Research progress and prospect of domestic pavement automatic testing system[J]. Chinese Hi-Tech Enterprises, 2009(19): 195-196.(in Chinese)
[10] 王建锋.激光路面三维检测专用车技术与理论研究[D].西安:长安大学,2010.
WANG Jian-feng. Research on vehicle technology on road three-dimension measurement[D]. Xi'an: Chang'an University, 2010.(in Chinese)
[11] 王 刚.路面病害光学无损检测技术[D].南京:南京理工大学,2007.
WANG Gang. The optical nondestructive examination for pavement distress—algorithm research based on super wavelet and multifractal theorem[D]. Nanjing: Nanjing University of Science and Technology, 2007.(in Chinese)
[12] 王荣本,王 超,初秀民.路面破损图像识别研究进展[J].吉林大学学报(工学版),2002,32(4):91-97.
WANG Rong-ben, WANG Chao, CHU Xiu-min. Developments of research on road pavement surface distress image recognition[J]. Journal of Jilin University(Engineering and Technology Edition), 2002, 32(4): 91-97.(in Chinese)
[13] CHENG H D, CHEN J R, GLAZIER C, et al. Novel
approach to pavement cracking detection based on fuzzy set theory[J]. Journal of Computing in Civil Engineering, 1999, 13(4): 270-280.
[14] 高建贞,任明武,杨静宇.一种快速实用的灰度校正算法[J].中国图象图形学报,2002,7(6):548-552.
GAO Jian-zhen, REN Ming-wu, YANG Jing-yu. A practical and fast method for non-uniform illumination correction[J]. Journal of Image and Graphics, 2002, 7(6): 548-552.(in Chinese)
[15] XU Zhi-gang, CHE Yan-li, MIN Hai-gen, et al. Initial
classification algorithm for pavement distress images using features fusion of texture and shape[C]∥TRB. Transportation Research Board 95th Annual Meeting. Washington DC: TRB, 2016: 1-17.
[16] ZHANG Da-qi, QU Shi-ru, HE Li, et al. Automatic ridgelet image enhancement algorithm for road crack image based on fuzzy entropy and fuzzy divergence[J]. Optics and Lasers in Engineering, 2009, 47(11): 1216-1225.
[17] ZUO Yong-xia, WANG Guo-qiang, ZUO Chun-cheng. Wavelet packet denoising for pavement surface cracks detection[C]∥IEEE. 2008 International Conference on Computational Intelligence and Security. New York: IEEE, 2008: 481-484.
[18] 唐 磊,赵春霞,王鸿南,等.路面图像增强的多偏微分方程融合法[J].中国图象图形学报,2008,13(9):1661-1666.
TANG Lei, ZHAO Chun-xia, WANG Hong-nan, et al. Fusion of multiple basic PDE models for enhancing road surface images[J]. Journal of Image and Graphics, 2008, 13(9): 1661-1666.(in Chinese)
[19] 李清泉,胡庆武.基于图像自动匀光的路面裂缝图像分析方法[J].公路交通科技,2010,27(4):1-5.
LI Qing-quan, HU Qing-wu. A pavement crack image analysis approach based on automatic image dodging[J]. Journal of Highway and Transportation Research and Development, 2010, 27(4): 1-5.(in Chinese)
[20] 王兴建,秦国锋,赵慧丽.基于多级去噪模型的路面裂缝检测方法[J].计算机应用,2010,30(6):1606-1609.
WANG Xing-jian, QIN Guo-feng, ZHAO Hui-li. Pavement crack detection method based on multilevel denoising model[J]. Journal of Computer Applications, 2010, 30(6): 1606-1609.(in Chinese)
[21] KIRSCHKE K R, VELINSKY S A. Histogram-based approach for automated pavement-crack sensing[J]. Journal of Transportation Engineering, 1992, 118(5): 700-710.
[22] 孙波成,邱延峻.路面裂缝图像处理算法研究[J].公路交通科技,2008,25(2):64-68.
SUN Bo-cheng, QIU Yan-jun. Pavement crack diseases recognition based on image processing algorithm[J]. Journal of Highway and Transportation Research and Development, 2008, 25(2): 64-68.(in Chinese)
[23] OLIVEIRA H, CORREIA P L. Automatic road crack segmentation using entropy and image dynamic thresholding[C]∥IEEE.Proceedings of the 17th European Signal Processing Conference. New York: IEEE, 2009: 622-626.
[24] CHENG H D, SHI X J, GLAZIER C. Real-time image
thresholding based on sample space reduction and interpolation approach[J]. Journal of Computing in Civil Engineering, 2003, 17(4): 264-272.
[25] 李 刚,贺昱曜.多方位结构元素路面裂缝图像边缘检测算法[J].计算机工程与应用,2010,46(1):224-226.
LI Gang, HE Yu-yao. Edge detection for road crack image with multidirection morphological structuring elements[J]. Computer Engineering and Applications, 2010, 46(1): 224-226.(in Chinese)
[26] 张 娟,沙爱民,孙朝云,等.基于相位编组法的路面裂缝自动识别[J].中国公路学报,2008,21(2):39-42.
ZHANG Juan, SHA Ai-min, SUN Zhao-yun, et al. Pavement crack automatic recognition based on phase-grouping method[J]. China Journal of Highway and Transport, 2008, 21(2): 39-42.(in Chinese)
[27] HUANG Ya-xiong, XU Bu-gao. Automatic inspection of pavement cracking distress[J]. Journal of Electronic Imaging, 2006, 15(1): 13-17.
[28] SORNCHAREAN S, PHIPHOBMONGKOL S. Crack
detection on asphalt surface image using enhanced grid cell analysis[C]∥IEEE.Proceedings of the 4th IEEE International Symposium on Electronic Design, Test and Applications. New York: IEEE, 2008: 49-54.
[29] MAODE Y, SHAOBO B, KUN X, et al. Pavement crack detection and analysis for high-grade highway[C]∥IEEE.Proceedings of the 8th International Conference on Electronic Measurement and Instruments. New York: IEEE, 2007: 548-552.
[30] 唐 磊,赵春霞,王鸿南,等.基于图像三维地形模型的路面裂缝自动检测[J].计算机工程,2008,34(5):20-21.
TANG Lei, ZHAO Chun-xia, WANG Hong-nan, et al. Automated pavement crack detection based on image 3D terrain model[J]. Computer Engineering, 2008, 34(5): 20-21.(in Chinese)
[31] 李 莉,孙立军,陈 长.适于路面破损图像处理的边缘检测方法[J].同济大学学报(自然科学版),2011,39(5):688-692.
LI Li, SUN Li-jun, CHEN Zhang. An edge detection method designed for pavement distress images[J]. Journal of Tongji University(Natural Science), 2011, 39(5): 688-692.(in Chinese)
[32] ZHANG A, LI Q, WANG K C P, et al. Matched filtering algorithm for pavement cracking detection[J]. Transportation Research Record, 2013(2367): 30-42.
[33] SUBIRATS P, FABRE O, DUMOULIN J, et al. A combined wavelet-based image processing method for emergent crack detection on pavement surface images[C]∥IEEE. Proceedings of the 12th European Signal Processing. New York: IEEE, 2004: 257-260.
[34] WANG K C P, LI Q, GONG W G. Wavelet-based pavement distress image edge detection with a trous algorithm[J]. Transportation Research Record, 2008(2024): 73-81.
[35] 吕 岩,曲仕茹.基于Beamlet变换的路面裂缝图像匀光算法[J].交通运输系统工程与信息,2011,11(5):123-128.
LYU Yan, QU Shi-ru. A pavement crack image dodging algorithm based on beamlet transform[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(5): 123-128.(in Chinese)
[36] 卢紫微,吴成东,陈东岳,等.基于分区域多尺度分析的路面裂缝检测算法[J].东北大学学报(自然科学版),2014,35(5):622-625.
LU Zi-wei, WU Cheng-dong, CHEN Dong-yue, et al. Pavement crack detection algorithm based on sub-region and multi-scale analysis[J]. Journal of Northeastern University(Natural Science), 2014, 35(5): 622-625.(in Chinese)
[37] MA Chang-xi, ZHAO Chun-xia, HOU Ying-kun. Pavement distress detection based on nonsubsampled contourlet transform[C]∥IEEE. 2008 International Conference on Computer Science and Software Engineering. New York: IEEE, 2008: 28-31.
[38] WANG Gang, XU Xiu-wei, XIAO Liang, et al. Algorithm based on the finite ridgeley transform for enhancing faint pavement cracks[J]. Optical Engineering, 2008, 47(47): 017004-1-10.
[39] 初秀民,王荣本.基于神经网络的沥青路面破损图像识别研究[J].武汉理工大学学报(交通科学与工程版),2004,28(3):373-376.
CHU Xiu-min, WANG Rong-ben. Asphalt pavement surface distress image recognition based on neural network[J]. Journal of Wuhan University of Technology(Transportation Science and Engineering), 2004, 28(3): 373-376.(in Chinese)
[40] 储江伟,初秀民,王荣本,等.沥青路面破损图象特征提取方法研究[J].中国图象图形学报,2003,8(10):1211-1217.
CHU Jiang-wei, CHU Xiu-min, WAGN Rong-ben, et al. Research on asphalt pavement surface distress image feature extraction method[J]. Journal of Image and Graphics, 2003, 8(10): 1211-1217.(in Chinese)
[41] OLIVEIRA H, CORREIA P L. Supervised strategies for cracks detection in images of road pavement flexible surfaces[C]∥IEEE. Proceedings of the 16th European Signal Processing Conference. New York: IEEE, 2008: 1-5.
[42] 胡 勇,赵春霞,郭志波.基于多尺度布朗运动模型的路面破损检测[J].计算机工程与应用,2008,44(3):234-235.
HU Yong, ZHAO Chun-xia, GUO Zhi-bo. Road crack detection based on multi-scale brown motion model[J]. Computer Engineering and Applications, 2008, 44(3): 234-235.(in Chinese)
[43] 王 华,朱 宁,王 祁.公路路面分形纹理特征分析和分类[J].哈尔滨工业大学学报,2005,37(6):816-818.
WANG Hua, ZHU Ning, WANG Qi. Fractal features analysis and classification for texture of pavement surface[J]. Journal of Harbin Institute of Technology, 2005, 37(6): 816-818.(in Chinese)
[44] 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 approach[J]. Journal of Harbin Institute of Technology, 2007, 39(1): 142-144.(in Chinese)
[45] 章秀华,洪汉玉,侯 佳,等.路面破损图像实时检测方法研究[J].电子设计工程,2009,17(6):36-37.
ZHANG Xiu-hua, HONG Han-yu, HOU Jia, et al. Research on real-time detection method for pavement surface distress image[J]. Electronic Design Engineering, 2009, 17(6): 36-37.(in Chinese)
[46] NGUYEN T S, BEGOT S, DUCULTY F, et al. Free-form anisotropy: a new method for crack detection on pavement surface images[C]∥IEEE.Proceedings of the 18th IEEE International Conference on. New York: IEEE, 2011: 1069-1072.
[47] 徐 威,唐振民,徐 丹,等.融合多特征与格式塔理论的路面裂缝检测[J].计算机辅助设计与图形学学报,2015,27(1):147-156.
XU Wei, TANG Zhen-min, XU Dan, et al. Integrating multi-features fusion and gestalt principles for pavement crack detection[J]. Journal of Computer-Aided Design and Computer Graphics, 2015, 27(1): 147-156.(in Chinese)
[48] 钱 彬,唐振民,沈肖波,等.基于多特征流形学习和矩阵分解的路面裂缝检测[J].仪器仪表学报,2016,37(7):1639-1646.
QIAN Bin, TANG Zhen-min, SHEN Xiao-bo, et al. Pavement crack detection based on multi-feature manifold learning and matrix factorization[J]. Chinese Journal of Scientific Instrument, 2016, 37(7): 1639-1646.(in Chinese)
[49] 徐志刚,赵祥模,宋焕生,等.基于直方图估计和形状分析的沥青路面裂缝识别算法[J].仪器仪表学报,2010,31(10):2260-2266.
XU Zhi-gang, ZHAO Xiang-mo, SONG Huan-sheng, et al. Asphalt pavement crack recognition algorithm based on histogram estimation and shape analysis[J]. Chinese Journal of Scientific Instrument, 2010, 31(10): 2260-2266.(in Chinese)
[50] 黄建平.基于二维图像和深度信息的路面裂缝检测关键技术研究[D].哈尔滨:哈尔滨工业大学,2013.
HUANG Jian-ping. Research on the key technologies of pavement crack inspection based on 2D image and depth information[D]. Harbin: Harbin Institute of Technology, 2013.(in Chinese)
[51] ALEKSEYCHUK O. Detection of crack-like indications in
digital radiography by global optimisation of a probabilistic estimation function[D]. Dresden: Dresden University of Technology, 2006.
[52] 李清泉,邹 勤,毛庆洲.基于最小代价路径搜索的路面裂缝检测[J].中国公路学报,2010,23(6):28-33.
LI Qing-quan, ZOU Qin, MAO Qing-zhou. Pavement crack detection based on minimum cost path searching[J]. China Journal of Highway and Transport, 2010, 23(6): 28-33.(in Chinese)
[53] ZHANG L, YANG F, ZHANG Y D, et al. Road crack
detection using deep convolutional neural network[C]∥IEEE. IEEE International Conference on Image Processing(ICIP). New York: IEEE, 2016: 3708-3712.
[54] CHA Y J, CHOI W, BÜYÜKÖZTÜRK O. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361-378.
[55] TONG Zheng, GAO Jie, HAN Zhen-qiang, et al. Recognition of asphalt pavement crack length using deep convolutional neural networks[J]. Road Materials and Pavement Design, 2018, 19(9): 1334-1349.
[56] TONG Zheng, GAO Jie, ZHANG Hai-tao. Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks[J]. Construction and Building Materials, 2017, 146: 775-787.
[57] TANAKA N, UEMATSU K. A crack detection method in road surface images using morphology[J].LAPR Workshop on Machine Vision Applications, 1998, 98: 154-157.
[58] 刘凡凡,徐国爱,肖 靖,等.基于连通域相关及Hough变换的公路路面裂缝提取[J].北京邮电大学学报,2009,32(2):24-28.
LIU Fan-fan, XU Guo-ai, XIAO Jing, et al. Cracking automatic extraction of pavement based on connected domain correlating and Hough transform[J]. Journal of Beijing University of Posts and Telecommunications, 2009, 32(2): 24-28.(in Chinese)
[59] DELAGNES P, BARBA D. A Markov random field for
rectilinear structure extraction in pavement distress image analysis[C]∥IEEE. Proceedings of International Conference on Image Processing. New York: IEEE, 1995: 446-449.
[60] 张洪光,王 祁,魏 玮.基于人工种群的路面裂纹检测[J].南京理工大学学报,2005,29(4):389-393.
ZHANG Hong-guang, WANG Qi, WEI Wei. Pavement distress detection based on artificial population[J]. Journal of Nanjing University of Science and Technology, 2005, 29(4): 389-393.(in Chinese)
[61] 李 刚.基于灰色系统理论的路面图像裂缝检测算法研究[D].武汉:武汉理工大学,2010.
LI Gang. Study on algorithms of pavement image crack detection based on the grey system theory[D]. Wuhan: Wuhan University of Technology, 2010.(in Chinese)
[62] 吴成东,卢佰华,陈东岳,等.基于方向特征及引力模型的路面裂缝检测[J].东北大学学报(自然科学版),2012,33(4):469-472.
WU Cheng-dong, LU Bai-hua, CHEN Dong-yue, et al. Pavement crack detection based on direction feature and gravitational model[J]. Journal of Northeastern University(Natural Science), 2012, 33(4): 469-472.(in Chinese)
[63] 徐 威,唐振民,吕建勇.基于图像显著性的路面裂缝检测[J].中国图象图形学报,2013,18(1):69-77.
XU Wei, TANG Zhen-min, LYU Jian-yong. Pavement crack detection based on image saliency[J]. Journal of Image and Graphics, 2013, 18(1): 69-77.(in Chinese)
[64] 钱 彬,唐振民,徐 威.基于稀疏自编码的路面裂缝检测[J].北京理工大学学报,2015,35(8):800-804,809.
QIAN Bin, TANG Zhen-min, XU Wei. Pavement crack detection based on sparesAutoEncoder[J]. Transactions of Beijing Institute of Technology, 2015, 35(8): 800-804, 809.(in Chinese)
[65] 王卫星,吴林春.基于分数阶积分谷底边界检测的路面裂缝提取[J].华南理工大学学报(自然科学版),2014,42(1):117-122.
WANG Wei-xing, WU Lin-chun. Extraction of pavement cracks based on valley edge detection of fractional integral [J]. Journal of South China University of Technology(Natural Science Edition), 2014, 42(1): 117-122.(in Chinese)
[66] 张德津,李清泉,陈 颖,等.基于空间聚集特征的沥青路面裂缝检测方法[J].自动化学报,2016,42(3):443-454.
ZHANG De-jin, LI Qing-quan, CHEN Ying, et al. Asphalt pavement crack detection based on spatial clustering feature[J]. Acta Automatica Sinica, 2016, 42(3): 443-454.(in Chinese)
[67] 宋宏勋,马 建,王建锋,等.基于双相机立体摄影测量的路面裂缝识别方法[J].中国公路学报,2015,28(10):18-25.
SONG Hong-xun, MA Jian, WANG Jian-feng, et al. Identification of pavement crack based on dual camera stereo photogrammetry[J]. China Journal of Highway and Transport, 2015, 28(10): 18-25.(in Chinese)
[68] 李 伟,呼延菊,沙爱民,等.基于3D数据和双尺度聚类算法的路面裂缝检测[J].华南理工大学学报(自然科学版),2015,43(8):99-105.
LI Wei, HU Yan-ju, SHA Ai-min, et al. Pavement crack detection based on two-scale clustering algorithm and 3D data[J]. Journal of South China University of Technology(Natural Science Edition), 2015, 43(8): 99-105.(in Chinese)
[69] 钱 彬,唐振民,徐 威,等.子块鉴别分析的路面裂缝检测[J].中国图象图形学报,2015,20(12):1652-1663.
QIAN Bin, TANG Zhen-min, XU Wei, et al. Pavement crack detection algorithm based on sub-patch discriminant analysis[J]. Journal of Image and Graphics, 2015, 20(12): 1652-1663.(in Chinese)
[70] 马常霞,赵春霞,胡 勇,等.结合NSCT和图像形态学的路面裂缝检测[J].计算机辅助设计与图形学学报,2009,21(12):1761-1767.
MA Chang-xia, ZHAO Chun-xia, HU Yong, et al. Pavement cracks detection based on NSCT and morphology[J]. Journal of Computer-Aided Design and Computer Graphics, 2009, 21(12): 1761-1767.(in Chinese)
[71] KAUL V, YEZZI A, TSAI Y J. Detecting curves with
unknown endpoints and arbitrary topology using minimal paths[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 1952-1965.
[72] LI Qing-quan, ZOU Qin, ZHANG Da-qiang, et al. FoSA: F* seed-growing approach for crack-line detection from pavement images[J]. Image and Vision Computing, 2011, 29(12): 861-872.
[73] OLIVEIRA H, CORREIA P L. Automatic road crack
detection and characterization[J]. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1): 155-168.
[74] ZOU Qin, CAO Yu, LI Qing-quan, et al. Crack tree: automatic crack detection from pavement images[J]. Pattern Recognition Letters, 2012, 33(3): 227-238.
[75] WU L L, MOKHTARI S, NAZEF A, et al. Improvement of crack-detection accuracy using a novel crack defragmentation technique in image-based road assessment[J]. Journal of Computing in Civil Engineering, 2014, 30(1): 04014118-1-19.
[76] CHUA K M, XU L. Simple procedure for identifying
pavement distresses from video images[J]. Journal of Transportation Engineering, 1994, 120(3): 412-431.
[77] ACOSTA J A, FIGUEROA J L, MULLEN R L. Algorithms for pavement distress classification by video image analysis[J]. Transportation Research Record, 1995(1505): 27-38.
[78] CHENG H D, MIYOJIM M. Novel system for automatic
pavement distress detection[J]. Journal of Computing in Civil Engineering, 1998, 12(3): 145-152.
[79] CHENG H D, CHEN J R, GLAZIER C, et al. Novel
approach to pavement distress detection based on fuzzy set theory[J]. Journal of Computing in Civil Engineering, 1999, 13(4): 270-280.
[80] WANG K C P, TEE W Y, WATKINS Q, et al. Digital
distress survey of airport pavement surface[C]∥Federal Aviation Administration. Federal Aviation Administration Airport Technology Transfer Conference. Washington DC: Federal Aviation Administration, 2002: 69-82.
[81] ZHOU J, HUANG P S, CHIANG F P. Wavelet-based
pavement distress detection and evaluation[J]. Optical Engineering, 2006, 45(2): 409-411.
[82] LEE B J, LEE H. Position-invariant neural network for
digital pavement crack analysis[J]. Computer-Aided Civil and Infrastructure Engineering, 2004, 19(2): 105-118.
[83] 丁爱玲,焦李成.基于支撑矢量机的路面破损识别[J].长安大学学报(自然科学版),2007,27(2):34-37.
DING Ai-ling, JIAO Li-cheng. Automation of recogniting pavement surface distress based on support vector machine[J]. Journal of Chang'an University(Natural Science Edition), 2007, 27(2): 34-37.(in Chinese)
[84] 肖旺新,张 雪,黄 卫.基于破损密度因子的路面破损识别新方法[J].交通运输工程与信息学报,2004,2(2):82-89.
XIAO Wang-xin, ZHANG Xue, HUANG Wei. A new method for distress automation recognition of pavement surface based on density factor and image processing[J]. Journal of Transportation Engineering and Information, 2004, 2(2): 82-89.(in Chinese)
[85] KASEKO M S, LO Z P, RITCHIE S G. Comparison of
traditional and neural classifiers for pavement-crack detection[J]. Journal of Transportation Engineering, 1994, 120(4): 552-569.
[86] NEJAD F M, ZAKERI H. An expert system based on
wavelet transform and radon neural network for pavement distress classification[J]. Expert Systems with Applications, 2011, 38(6): 7088-7101.
[87] NEJAD F M, ZAKERI H. A comparison of multi-resolution methods for detection and isolation of pavement distress[J]. Expert Systems with Applications, 2011, 38(3): 2857-2872.
[88] NEJAD F M, ZAKERI H. An optimum feature extraction method based on wavelet-radon transform and dynamic neural network for pavement distress classification[J]. Expert Systems with Applications, 2011, 38(8): 9442-9460.
[89] 段 瑗,李春书,闫 尧.基于支持向量机的路面图像分类方法[J].河北农业大学学报,2016,39(6):124-129.
DUAN Yuan, LI Chun-shu, YAN Yao. Terrain classification method based on the support vector machine[J]. Journal of Agricultural University of Hebei, 2016, 39(6): 124-129.(in Chinese)
[90] KOCH C, BRILAKIS I. Pothole detection in asphalt pavement images[J]. Advanced Engineering Informatics, 2011, 25(3): 507-515.
[91] RADOPOULOU S C, JOG G M, BRILAKIS I. Patch
distress detection in asphalt pavement images[C]∥ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction.Vilnius: Vilnius Gediminas Technical University, 2013, 30: 1-9.
[92] TSAI Y C, KAUL V, MERSEREAU R M. Critical assessment of pavement distress segmentation methods[J]. Journal of Transportation Engineering, 2014, 136(1): 11-19.
[93] 沙爱民,童 峥,高 杰.基于卷积神经网络的路表病害识别与测量[J].中国公路学报,2018,31(1):1-10.
SHA Ai-min, TONG Zheng, GAO Jie. Recognition and measurement of pavement disasters based on convolutional neural networks[J]. China Journal of Highway and Transport, 2018, 31(1): 1-10.(in Chinese)

Memo

Memo:
-
Last Update: 2019-02-28