Novel Automatic Pavement Crack Detection Algorithm

doi:10.16182/j.issn1004731x.joss.201709018

Abstract

Abstract: The complexity of noises covers a wide area of actual road images which causes that it is difficult to detect cracks. An automatic pavement crack detection algorithm was proposed in view of the characteristics of crack image in pavement disease. Gray-scale correction and filtering was used to preprocess the crack image. The maximum interclass variance method and Canny operator were used to detect the edge of the disease image, and then the localization and accurate segmentation algorithm was proposed for the crack image based on the maximum connectivity of the crack in the fracture image. The convolution neural network algorithm was used to recognize the pavement cracks. The experimental results show that the proposed method is superior to other advanced algorithms on the crack detection efficiency, and robust to the different types of crack images.

Key words: pavement cracks, image segmentation, maximum connected domains, convolution neural network

CLC Number:

TP391.4

Gao Shangbing, Xie Zheng, Pan Zhigeng, Qin Fangzhe, Li Rui. Novel Automatic Pavement Crack Detection Algorithm[J]. Journal of System Simulation, 2017, 29(9): 2009-2015.

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