Research on pavement crack detection technology based on convolution neural network

被引：0
|
作者：
Zhang W. [1 ]
Zhong J. [1 ]
Yu J. [2 ]
Ma T. [1 ]
Mao S. [1 ]
Shi Y. [1 ]
机构：
[1] School of Transportation, Southeast University, Nanjing
[2] School of Civil Engineering, Henan Polytechnic University, Jiaozuo
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2021年 / 52卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Convolution neural network; Crack geometry characteristics; Image processing; Pavement crack;
D O I：
10.11817/j.issn.1672-7207.2021.07.026
中图分类号：
学科分类号：
摘要：
Based on machine learning, a fast detection algorithm of pavement cracks was designed, and a convolution neural network was built to collect and process the asphalt pavement image. The effect of two kinds of neural network models, multilayer perceptron and convolutional neural network, in asphalt pavement state recognition was analyzed. The high-precision convolution neural network recognition algorithm was used to improve the efficiency of image recognition. The recognition accuracy of the two types of models was compared and analyzed with the help of confusion matrix. Three kinds of processing methods of extracting crack image were compared, which were spatial domain filtering, threshold binarization and morphological filtering. The results show that the accuracy of the convolutional neural network model is 99.75%, which is higher than that of the multi-layer perceptron. It can recognize four kinds of crack images with high accuracy, including noncrack, transverse crack, longitudinal crack and alligator crack. Median filtering algorithm can extract the length, width and area of pavement cracks effectively, and the research results can be used for rapid detection of pavement cracks. © 2021, Central South University Press. All right reserved.
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页码：2402 / 2415
页数：13
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