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
1 《中国公路学报》编辑部. 中国桥梁工程学术研究综述·2021[J]. 中国公路学报, 2021, 34(2): 1-97.
Editorial Department of China Journal of Highway and Transport. A summary of academic research on chinese bridge engineering·2021[J]. Journal of China Highway, 2021, 34(2): 1-97.
2 Song B, Wei N. Statistics properties of asphalt pavement images for cracks detection[J]. Journal of Information & Computational Science, 2013, 10(9): 2833-2843.
3 Zou Q, Zhang Z, Li Q Q, et al. Deepcrack: learning hierarchical convolutional features for crack detection[J]. IEEE Trans Image Processing, 2019, 28(3): 1498-1512.
4 肖明尧, 李雄飞, 张小利, 等.基于多尺度的区域生长的图像分割算法[J].吉林大学学报: 工学版, 2017, 47(5): 1591-1597.
Xiao Ming-yao, Li Xiong-fei, Zhang Xiao-li, et al. Image segmentation algorithm based on multi-scale region growth[J]. Journal of Jilin University(Engineering and Technology Edition), 2017, 47(5): 1591-1597.
5 Qu Z, Chen S Q, Liu Y Q, et al. Linear seam elimination of tunnel crack images based on statistical specific pixels ratio and adaptive fragmented segmentation[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21: 3599-3607.
6 谢志江, 吕波, 刘琴, 等.旋转不变性图像模板匹配快速算法[J].吉林大学学报: 工学版, 2013, 43(3): 711-717.
Xie Zhi-jiang, Bo LYU, Liu Qin, et al. A fast algorithm for image template matching with rotation invariance[J]. Journal of Jilin University(Engineering and Technology Edition), 2013, 43(3): 711-717.
7 Zhao B, Dai M, Li P, et al. Defect detection method for electric multiple units key components based on deep learning[J]. IEEE Access, 2020, 8: 136808-136818.
8 李良福, 马卫飞, 李丽, 等.基于深度学习的桥梁裂缝检测算法研究[J].自动化学报, 2019, 45(9): 1727-1742.
Li Liang-fu, Ma Wei-fei, Li Li, et al. Research on bridge crack detection algorithm based on deep learning[J]. Automatica Sinica, 2019, 45(9): 1727-1742.
9 Cha Y J, Choi W, Oral B. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361-378.
10 He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]∥IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-778.
11 Rahul K, Ankur G, Harkirat S A, et al. CBSN: comparative measures of normalization techniques for brain tumor segmentation using SRCNet[J]. Multimedia Tools and Applications, 2022, 81(10): 13203-13235.
12 曹锦纲, 杨国田, 杨锡运.基于注意力机制的深度学习路面裂缝检测[J].计算机辅助设计与图形学学报, 2020, 32(8): 1324-1333.
Cao Jin-gang, Yang Guo-tian, Yang Xi-yun. Deep learning pavement crack detection based on attention mechanism[J]. Journal of Computer Aided Design and Graphics, 2020, 32(8): 1324-1333.
13 黄宏伟, 李庆桐.基于深度学习的盾构隧道渗漏水病害图像识别[J].岩石力学与工程学报, 2017, 36(12): 2861-2871.
Huang Hong-wei, Li Qing-tong. Image recognition of water leakage in shield tunnels based on deep learning[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(12): 2861-2871.
14 Simonyan S, Zisserman A. Very deep convolutional networks for large-scale image recognition[J/OL]. [2015-04-10].
15 Islam M M M, Kim JM. Vision-based autonomous crack detection of concrete structures using a fully convolutional encoder-decoder network[J]. Sensors(Basel), 2019(19): 19194251.
16 薛亚东, 李宜城.基于深度学习的盾构隧道衬砌病害识别方法[J].湖南大学学报: 自然科学版, 2018, 45(3): 100-109.
Xue Ya-dong. Li Yi-cheng. A method of disease recognition forshield tunnel lining based on deep learning[J]. Joumal of Hunan University(Natural Sciences), 2018, 45(3): 100-109.
17 Sandler M, Howard A, Zhu M, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]∥IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 4510-4520.
18 Redmon J, Farhadi A. YOLOv3: an incremental improvement[J/OL]. [2018-04-08].
19 蔡逢煌, 张岳鑫, 黄捷.基于YOLOv3与注意力机制的桥梁表面裂痕检测算法[J].模式识别与人工智能, 2020, 33(10): 926-933.
Cai Feng-huang, Zhang Yue-xin, Huang Jie. Bridge surface crack detection algorithm based on YOLOv3 and attention mechanism[J]. Pattern Recognition and Artificial Intelligence, 2020, 33(10): 926-933.
20 Saouma V E, Barton C C, Gamaleldin N A. Fractal characterization of fracture surfaces in concrete[J]. Engineering Fracture Mechanics, 1990, 35(1-3): 47-53.
21 Xu F Y, Jiang Q S. Dynamic obstacle surmounting analysis of a bilateral-wheeled cable-climbing robot for cable-stayed bridges[J]. The Industrial Robot, 2019, 46(3): 431-443.
22 李超飞.桥梁病害检测的无人机地面站软件设计与实现[D].西安: 长安大学电子与控制工程学院, 2020.
Li Chao-fei. Design and implementation of UAV ground station software for bridge disease detection [D]. Xi'an: School of Electronics and Control Engineering, Chang'an University, 2020.
23 Howard A G, Zhu M, Chen B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J/OL]. [2017-04-17].
24 Shi Y, Cui L M, Qi Z Q, et al. Automatic road crack detection using random structured forests[J]. IEEE Ttransactions on Intelligent Transportation Systems, 2016, 17(12): 3434-3445.
25 Billah U H, La H M, Tavakkoli A. Deep learning-based feature silencing for accurate concrete crack detection[J]. Sensors, 2020, 20(16): 20164403.
26 Jiang W B, Liu M, Peng Y N, et al. HDCB-Net: a neural network with the hybrid dilated convolution for pixel-level crack detection on concrete bridges[J]. IEEE Transactions on Industrial Informatics, 2021, 17(8): 5485-5494.
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