Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (5): 1418-1426.doi: 10.13229/j.cnki.jdxbgxb.20210860

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Crack identification method for bridge based on BCEM model

Zhen-hai ZHANG1(),Kun JI1,Jian-wu DANG1,2   

  1. 1.School of Automation and Electrical Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China
    2.Gansu Province Artificial Intelligence and Graphics and Image Processing Engineering Research Center，Lanzhou 730070，China
  • Received:2021-09-02 Online:2023-05-01 Published:2023-05-25

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Abstract:

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.

Key words: computer application, deep learning, crack detection, image processing, convolutional neural network

CLC Number: 

  • TP391.41

Fig.1

Dataset expansion process"

Fig.2

UAV crack collection in Zhongshan Bridge，Lanzhou"

Fig.3

Image of DW convolution structure"

Fig.4

BC-MobileNet network structure diagram"

Fig.5

Schematic diagrams of misdetection and undetection"

Fig.6

Patches connection method"

Fig.7

Mapping process"

Fig. 8

Contrast of gray histogram envelopes"

Fig.9

Crack extraction results"

Table 1

Influence of preprocessing method for BC-MobileNet model recognition"

测试集面元总数预处理模型正确识别数准确率/%
500否39879.6
500传统方法38977.8
500本文方法46392.6

Fig.10

Effect of preprocessing on model convergence"

Table 2

Influence of various models on patches recognition"

主干网络测试集面元数面元规格/像素识别准确率/%识别召回率/%
VGG-1650016×1616.762.4
MobileNetV250016×1667.273.6
MobileNetV2-A5008×840.752.8
MobileNetV2-B50032×3276.883.0
MobileNetV2-C50016×1671.885.3
MobileNetV2-D50016×1675.081.8
BC-MobileNet50016×1692.694.1

Table 3

Influence of BCEM and target detection model on the accuracy of panel recognition"

模型测试集面元数平均识别IoUAP识别速度/（s·面元-1）模型大小/Mbit
BCEM5000.980.95551.76219.9
Faster-Rcnn5000.870.90731.152127
YoLoV35000.820.90190.08969

Fig.11

Extraction results of cracks by various algorithm models"

Table 4

Analysis of fracture index extracted by various models"

模 型测试集图片数pr/%re/%模型大小/Mbit
Otsu10015.716.3-
最大熵分割10039.752.4-
漫水填充1007.87.5-
FCN10072.275.239
U-Net10081.783.428
ANet-FSM［25］10088.4687.040
HDCB-Net［26］10091.4486.3564
BCEM10097.598.719.9
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