Title: Application of crack detection algorithm using convolutional neural network in concrete pavement construction

Authors: Wuqiang Wei; Xiaoyan Xu

Addresses: Zhengzhou University of Science & Technology, Zhengzhou 450064, Henan, China ' Institute of Economic and Trade, Henan University of Technology, Zhengzhou 450001, Henan, China; Zhengzhou University of Science & Technology, Zhengzhou 450064, Henan, China

Abstract: Concrete pavement cracks detection is specifically studied based on the application principle of Convolutional Neural Network (CNN) in large-scale image recognition and processing. Firstly, the feature extraction and selection principle are introduced for sample data of concrete pavement crack, and the sample Image Processing (IP) method is expounded. Secondly, the pre-processing of concrete pavement crack images is proposed. Through the establishment of the functional relationship between Crack Rate (CP) and Crack Rate Index (CRI), the samples are trained, and the crack detection model based on CNN is implemented. The CNN model is evaluated by comparing the accuracy and loss rate of the CNN model with the traditional Alex model in processing different numbers of images. Afterward, a concrete pavement crack detection platform is developed with cross-platform Python, OpenCV, and QT framework, combined with Deep Learning (DL), graphical interface development, and image processing. Thereupon, a concrete pavement health evaluation method is proposed. Regression analysis shows that the evaluation method can reasonably evaluate concrete pavement crack.

Keywords: convolutional neural network; crack detection; Gaussian filtering algorithm; Canny algorithm; concrete pavement.

DOI: 10.1504/IJGUC.2022.124406

International Journal of Grid and Utility Computing, 2022 Vol.13 No.2/3, pp.154 - 163

Received: 20 Mar 2021
Accepted: 16 Sep 2021
Published online: 26 Jul 2022 *

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