|Table of Contents|

Surface crack identification method of concrete bridge based on DC-Unet(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年3期
Page:
66-75
Research Field:
桥梁与隧道工程
Publishing date:

Info

Title:
Surface crack identification method of concrete bridge based on DC-Unet
Author(s):
MA Ya-fei;  SUN Wen-kang;  HE Yu;  WANG Lei
(School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, Hunan, China)
Keywords:
bridge engineering;  concrete crack;  U-net structure;  dense residual connection;  atrous spatial pyramid pooling;  damage detection
PACS:
U445.7
DOI:
10.19721/j.cnki.1671-8879.2024.03.006
Abstract:
To solve the problem of largely false detection rate and highly background noise for the surface damage detection of existing concrete bridges, a surface crack damage identification method was proposed based on DC-Unet. Firstly, the dense residual connection module(DRCM)was used to replace the convolution operation before each downsampling, and upsampling and the last 1×1 convolution in the U-net model, which increased the model depth. Secondly, the atrous spatial pyramid pooling module was integrated into the position before the first upsampling of the model, which expanded the receptive field of the model and improved the ability of the model to obtain multi-level apparent fracture characteristics. Finally, the convolutional block attention module combined with space and channel was integrated into the DRCM structure in a residual connection way, which improved the model's attention to the apparent crack feature area. The Labelme software was used to label 50 apparent crack images with a resolution of 3 648 pixel×2 736 pixel, and a TimCracks dataset containing 1 347 concrete apparent crack images and label images was constructed based on the window sliding algorithm. The proposed concrete bridge apparent crack recognition method was compared with U-net model, SegNet model, U-net++ model, traditional OTSU threshold segmentation algorithm and edge detection algorithm based on Canny operator. The results show that the proposed method can accurately segment and identify the apparent cracks of concrete bridges, and has the advantages of high precision and good noise resistance. It can effectively reduce the false detection rate under the condition of coating interference and unsmooth folds in the background of apparent crack images. The accuracy, intersection over union and F1-score of crack recognition are 96.28%, 73.80% and 84.91%, respectively. The three evaluation indexes are improved, compared with U-net model, SegNet model and U-net++ model. Compared with the traditional crack segmentation algorithm, the proposed DC-Unet network solves the problem of false detection of traditional methods. The cracks can be effectively segmented from the coating background.3 tabs, 13 figs, 28 refs.

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Last Update: 2024-05-01