Crack identification method for bridge based on BCEM model

被引：0
|
作者：
Zhang Z.-H. [1 ]
Ji K. [1 ]
Dang J.-W. [1 ,2 ]
机构：
[1] School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou
[2] Gansu Province Artificial Intelligence and Graphics and Image Processing Engineering Research Center, Lanzhou
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2023年 / 53卷 / 05期
关键词：
computer application; convolutional neural network; crack detection; deep learning; image processing;
D O I：
10.13229/j.cnki.jdxbgxb.20210860
中图分类号：
学科分类号：
摘要：
To realize high-efficiency，light-weight，and non-contact bridge crack disease identification，a bridge crack identification network based on the（bridge crack extraction model，BCEM）is proposed. The network combines deep learning with traditional image processing methods. First，the crack image is preprocessed to enhance the expression of crack information. Then the sliding window method is used to divide the crack into patches. According to the characteristics of the patches，the improved lightweight model named BC-MobileNet is used to classify crack features. Finally，misdetected and undetected cracks are identified to achieve accurate identification of bridge cracks. Compared with different crack identification methods such as target detection and pattern recognition，the results show that the BCEM has improved in various experimental indicators， which proves the effectiveness of this network for bridge crack identification. © 2023 Editorial Board of Jilin University. All rights reserved.
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页码：1418 / 1426
页数：8
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