Bridge defects detection and quantifying method based on modified Faster R-CNN and U-Net

被引：0
|
作者：
Qiao P. [1 ]
Liang Z. [2 ]
Duan C. [1 ]
Ma C. [1 ]
Wang S. [1 ]
Di J. [3 ]
机构：
[1] School of Civil Engineering, Chang'an University, Xi'an
[2] School of Highway, Chang'an University, Xi'an
[3] School of Civil Engineering, Chongqing University, Chongqing
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2024年 / 54卷 / 03期
关键词：
bridge structure; crack dimension determination; improved faster region convolutional neural networks (Faster R-CNN); improved U-Net; surface disease recognition;
D O I：
10.3969/j.issn.1001-0505.2024.03.013
中图分类号：
学科分类号：
摘要：
In order to realize the automated detection of bridge defects,the intelligent identification and dimension measurement of surface diseases of concrete bridges was studied based on the digital image processing technology. The disease identification method based on improved faster region convolutional neural networks (Faster R-CNN)algorithm was improved,the design of region proposal network (RPN)anchors was optimized using K-means clustering and genetic algorithm. Based on the predicting region of strip crack disease, the crack morphology was extracted by combining the ResNet34 algorithm and U-Net segmentation method. Finally,the pixel width and length of crack were calculated by crack morphology. The results show that the optimized design of RPN anchors can improve the recognition effect of Faster R-CNN algorithm for surface diseases. The prediction accuracy,average recall and average precision of five common diseases are increased from 68. 40% to 85. 40%,69. 87% to 83. 59% and 74. 64% to 83. 72%,respectively. The automated dimension measurement of crack diseases can be realized using the diseases predicted anchor and pixel dimension calculation of improved U-Net algorithm. The improved Faster R-CNN and U-Net algorithm can realize the intelligent identification and quantification of common diseases in concrete bridges,contributing to improve the efficiency of bridge disease detection and promote the intelligence of bridge technical condition assessment. © 2024 Southeast University. All rights reserved.
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页码：627 / 638
页数：11
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