Mechanical Engineering Science

Cement Pavement Surface Crack Detection Based on Image Processing

LeiHongwei, ChengJianlian, XuQi

Abstract


This article introduces the application of image recognition technology in cement pavement crack detection and put forward to method for determining threshold about grayscale stretching. This algorithm is designed for binarization which has a self-adaptive characteristic. After the image is preprocessed, we apply 2D wavelet and Laplace operator to process the image. According to the characteristic of pixel of gray image, an algorithm designed on binarization for Binary image. The feasibility of this method can be verified the image processed by comparing with the results of three algorithms: Otsu method, iteration method and fixed threshold method.

Keywords


Pavement crack detection; Grayscale stretching; Self-adaptive; 2D Wavelet; Laplace; Binarization

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References


CHUO Er-yong, Development Summary of International Pavement Surface Distress Automatic Survey System [J]. Transport Standardization, 2009, 09: 96-97;

FAN Jiu-lun, ZHAO Feng, Two-Dimensional Otsu’s Curve Thresholding Segmentation Method for Gray-Level Images [J], Acta Electronica Sinica,2007,35(4):751-755;

Lakhwinder KAUR,Savita GUPTA,R.C. CHAUHAN. Image Denoising using Wavelet Thresholding [A]. Proceedings of the Third Indian Conference on Computer Vision, Graphics & Image Processing [C]. Ahmadabad, India: Allied Publishers Private Limited, 2002, 11:1522-1531;

DONG Hong-yan, WANG Lei, LI Ji-cheng, SHEN Zhen-kang, Multiscale edge detection based on Laplacian pyramid [J], Opto-Electronic Engineering,2007,7(34):135-140;

Ohtsu N.A threshold selection method from gray-level histograms [J].IEEE Transactions on Systems Man & Cybernetics,2007,9(1):62-66.

Ferman A M,Tekalp A M,Mehrotra R.Robust color histogram descriptors for video segment retrieval and identification [J].IEEE Transactions on Image Processing;A Publication of the IEEE Signal Processing Society, 2002,11(5):497-508;

Zhang C,Xie Y,Liu D,et al.Fast threshold image segmentation based on 2D fuzzy fisher and random local optimized QPSO [J].IEEE Transactions on Image Processing, 2017, 26(3): 1355-1362;

Qi Lin,Wang Jing,Chen Yanlei,et al. Research on the image segmentation of icing line based on NSCT and 2-D OSTU[C], International Conference on Modelling, Identification and Control, 2015: 1-5;

Liu S.Image segmentation technology of the Ostu method for image materials based on binary PSO algorithm[M], Advances in computer science, intelligent system and environment. Berlin Heidelberg: Springer, 2011:415-419;

PENG Bo1, JIANG Yang-sheng, HAN Shi-fan1, LUO Nanxin, A Review of Automatic Pavement Crack Image Recognition Algorithms[J], Journal of Highway and Transportation Research and Development, 2014, 7, 7(31): 10-25;

Gu Mehua, Su Binbin, Wang Miaomiao, Wang Zhilei, Survey on decolorization methods[J], Application Research of Computers, 2018, 36(5):

Hideyuki Tamura , Computer image processing technology [M]。Beijing Normal University Publishing House,1986;

Rafael C. Gonzalez, Richard E. Woods,Digital image processing [M], Publishing House of Electronics Industry,2017;

马拉特,A wavelet tour of signal processing[M].China Machine Press,2003

Angel P,Morris C,Analyzing the Mallat wavelet transform to delineate contour and textural features[J].Computer Vision and Image Understanding,2000,80 ( 3) :

-288;

D.L.Donoho, I.M.JohnStone.Ideal spatial adaptation via

wavelet shrinkage[J]. Biometrika, 1994, 81: 425-455;

Otsu N.A threshold selection method from gray-level histo-grams[J].IEEE Transactions on Systems,Man and Cyber-netics,1979,9(1):62-66

Castleman, K. Digital Image Processing. Prentice-Hall Signal Pro-cessing Series, 1979.




DOI: https://doi.org/10.33142/me.v1i1.661

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