A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK

LU Jiaqi; YAO Zhidong

doi:10.13204/j.gyjzG20112504

Volume 51 Issue 5

Sep. 2021

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2021 > 51(5): 30-36

YANG Yuan, CUI Qiandao, LIAN Jijian, LIU Hongbo, ZHOU Guangen, CHEN Zhihua. LSTM-BASED DAMAGE PREDICTION AND ASSESSMENT OF SPATIAL FRAME STRUCTURE[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(7): 203-208. doi: 10.13204/j.gyjzG20092308

Citation:

LU Jiaqi, YAO Zhidong. A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(5): 30-36. doi: 10.13204/j.gyjzG20112504

Citation:

LU Jiaqi, YAO Zhidong. A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(5): 30-36. doi: 10.13204/j.gyjzG20112504

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A CONCRETE CRACK RECOGNITION METHOD BASED ON PROGRESSIVE CASCADE CONVOLUTION NEURAL NETWORK

doi: 10.13204/j.gyjzG20112504

LU Jiaqi^1,2,
YAO Zhidong^1,2

1. Central Research Institute of Building and Construction (Shenzhen) Co., Ltd., Shenzhen 518055, China;
2. Shenzhen Engineering Research Center of Intelligent Inspection for Curtain Wall, Shenzhen 518055, China

Received Date: 2020-11-25
Available Online: 2021-09-16
Publish Date: 2021-09-16

Abstract

Abstract

Convolution neural network method of deep learning is a high robust method for image crack recognition at present, which is mainly divided into sliding window method and image segmentation method. Sliding window method has the problems of low precision of later threshold segmentation of cracks; global image segmentation method has the problem of serious unbalanced sample distribution between crack region and background region,which will affect the accuracy of crack segmentation. The method based on progressive cascade convolution neural network was used to detect concrete surface cracks:firstly, the fully convolution neural network was used to judge whether there were cracks in all the dense overlapped window areas in the image only once, and then the window blocks with cracks were extracted as the region of interest, and then the light-weight U-Net image segmentation network was used to act on the region of interest to extract the crack area accurately. Experimental results showed that the proposed progressive cascade convolution neural network was superior to sliding window method and global image segmentation method, and had a reliable application prospect.
- crack recognition,
- progressive cascade convolution neural network,
- fully convolutional network,
- region of interest,
- U-Net image segmentation

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References

[1]	FENG D, FENG M Q. Computer Vision for SHM of Civil Infrastructure:from Dynamic Response Measurement to Damage Detection:A Review[J]. Engineering Structures, 2018, 156:105-117.
[2]	KONG X, LI J. Non-Contact Fatigue Crack Detection in Civil Infrastructure Throughimage Overlapping and Crack Breathing Sensing[J]. Automation in Construction, 2019, 99:125-139.
[3]	OLIVEIRA H, CORREIA P L. Automatic Road Crack Segmentation Using Entropy and Image Dynamic Thresholding[C]//Proceedings of the 2009 17th European Signal Processing Conference. Glasgow, U K:2009:622-626.
[4]	QUINTANA M, TORRES J, MENéNDEZ J M. A Simplified Computer Vision System for Road Surface Inspection and Maintenance[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(3):608-619.
[5]	AYENU-PRAH A, ATTOH-OKINE N.Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition[J]. EURASIP Journal on Advances in Signal Processing, 2008(1). DOI: 10.1155/2008/861701.
[6]	ACHARYA T, TSAI P S. Edge-Detection Based Noise Removal Algorithm:US 6229578 B1[P]. 1997-12-08.
[7]	LI Q Q, LIU X L.Novel Approach to Pavement Image Segmentation Based on Neighboring Difference Histogram Method[C]//Proceedings of the 2008 Congress on Image and Signal Processing.Sanya:2008:792-796.
[8]	JIN H Z, WAN F, YE Z W. Pavement Crack Detection Fused HOG and Watershed Algorithm of Range image[J]. Journal of Huazhong Normal University(Natural Sciences), 2017, 51(5):715-722.
[9]	徐洋. 基于计算机视觉的桥梁结构局部损伤识别方法研究[D]. 哈尔滨:哈尔滨工业大学, 2019.
[10]	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.
[11]	王森, 伍星, 张印辉, 等. 基于深度学习的全卷积网络图像裂纹检测[J]. 计算机辅助设计与图形学学报, 2018, 30(5):859-867.
[12]	曹锦纲, 杨国田, 杨锡运, 等. 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报, 2020, 32(8):1324-1333.
[13]	任秋兵, 李明超, 沈扬, 等.水工混凝土裂缝像素级形态分割与特征量化方法研究[J]. 水力发电学报, 2021, 40(2):234-246.
[14]	OTSU N. A Threshold Selection Method from Gray-Level Histogram[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1):62-66.
[15]	王超, 贾贺, 张社荣, 等. 基于图像的混凝土表面裂缝量化高效识别方法[J]. 水力发电学报, 2021, 40(3):134-144.
[16]	GONZALEZ R C, WOODS R E, EDDINS S L. 数字图像处理的MATLAB实现[M]. 阮秋琦, 译.北京:清华大学出版社, 2013.
[17]	李刚, 贺拴海, 巨永锋, 等. 远距离混凝土桥梁结构表面裂缝精确提取算法[J]. 中国公路学报, 2013, 26(4):102-108.
[18]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal Loss for Dense Object Detection[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017:2980-2988.
[19]	RONNEBERGER O, FISCHER P, BROX T. U-Net:Convolutional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.2015:234-241.
[20]	SERMANET P, EIGEN D, ZHANG X, et al. OverFeat:Integrated Recognition, Localization and Detection Using Convolutional Networks[C]//International Conference on Learning Representations.2014:1-16.

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