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

Turn off MathJax

Article Contents

Abstract

References

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

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

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

PDF( 7818 KB)

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

FullText(HTML)

References(20)

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.

Relative Articles

[1]	WANG Mingjun, SU Zhiwen, CHEN Bingcong, LIU Airong. Crack Segmentation of Underwater Structures of Bridges Based on Hierarchical Feature Residual Neural Network[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(3): 126-132. doi: 10.3724/j.gyjzG23030303
[2]	WAN Neng, HUANG Minshui, ZHU Hongping. Research on Two-Stage Damage Identification of Steel Frame Based on CNN and CMCM[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(1): 123-129. doi: 10.3724/j.gyjzG23072612
[3]	FAN Cunjun, JIN Songyan, JIN Nan, SHI Zhongqi, WU Yongjingbang, HAO Xintian. Crack Recognition and Quantitative Analysis Based on Deep Learning[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(8): 126-132. doi: 10.3724/j.gyjzG24061802
[4]	FAN Lijun. Identification of Crack in Concrete Structures Based on MobileNetV2 of Lightweight Convolutional Network[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(7): 231-236. doi: 10.13204/j.gyjzG23021618
[5]	LI Shujin, XIONG Shuqi, FAN Peiran, WANG Gang. Application Research on Deep Convolutional Neural Network Considering Residual Learning in Structural Damage Identification[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(7): 192-198. doi: 10.13204/j.gyjzg21101009
[6]	SONG Chenghao, CHEN Shucheng, HU Xiaobin, YUAN Huanxin. Crack Monitoring of RC Columns Under Cyclic Loading Based on Computer Vision[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 53-60. doi: 10.13204/j.gyjzG22070112
[7]	HU Wenkui, DENG Hui, FU Zhixu, AN Dongyang, DUAN Rui. Bridge Crack Segmentation and Measurement Method Based on Full Convolutional Neural Network[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(4): 192-201,218. doi: 10.13204/j.gyjzG21053111
[8]	LI Rui, ZHANG Chun. RESEARCH ON STRUCTURAL DAMAGE DETECTION METHOD BASED ON ONE-DIMENSIONAL DILATED CONVOLUTION NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(10): 177-183. doi: 10.13204/j.gyjzg20103011
[9]	HU Weibing, YANG Jia, WANG Long, HOU Yanfang. STUDY ON DAMAGE DETECTION AND QUANTIFICATION OF ANCIENT BUILDING TIMBER STRUCTURES BASED ON LAMINATION THEORY AND BP NEURAL NETWORKS[J]. INDUSTRIAL CONSTRUCTION, 2020, 50(11): 71-77,111. doi: 10.13204/j.gyjzG20020501
[10]	Zhou Qiujuan, Chen Xiaoping, Zeng Lingling. RESEARCH ON NEURAL NETWORK MODEL OF DEFORMATION BEHAVIOR OF SOFT SOIL[J]. INDUSTRIAL CONSTRUCTION, 2008, 38(6): 48-53. doi: 10.13204/j.gyjz200806014
[11]	Hong Jinxiang, Huang Wei, Miao Changwen. STUDY ON PREDICTION OF CONCRETE STRENGTH USING WAVELET NEURAL NETWORK[J]. INDUSTRIAL CONSTRUCTION, 2004, 34(7): 47-49. doi: 10.13204/j.gyjz200407013
[12]	Ling Daijian. A METHOD OF DAYLIGHT DESIGN OF MILL BUILDING BASED ON ANN[J]. INDUSTRIAL CONSTRUCTION, 2004, 34(11): 24-26. doi: 10.13204/j.gyjz200411007

Supplements(0)

Cited By

Cited by

Periodical cited type(7)

1.	陈善继，刘天禹，刘鹏宇，黄凯，李瑶瑶. 基于计算机视觉的公路边坡裂缝监测方法. 北京工业大学学报. 2024(06): 702-710 .
2.	江涛锋，唐永圣，陆志溢，沈国根. 基于语义分割的混凝土裂缝图像识别方法研究. 工程勘察. 2023(04): 42-47 .
3.	孙亚辉. 喷射混凝土隧道衬砌的生命周期评估优化. 广东交通职业技术学院学报. 2023(03): 15-19+40 .
4.	朱飞，姚腾，曾梓逸，董鹏宇，缪长青. 基于数字图像的混凝土梁裂缝识别试验. 南京工业大学学报(自然科学版). 2023(05): 537-545 .
5.	姚志东，卢佳祁，熊梦雅，卢炜. 基于计算机视觉的钢结构表面缺陷智能识别研究综述. 建筑结构. 2023(24): 126-135 .
6.	王宁，柴雪松，暴学志，李健超，马学志，田德柱. 无砟道床表观伤损智能识别算法研究. 铁道建筑. 2022(04): 22-26+63 .
7.	赵青羽. 基于极点特征聚类的智能建筑混凝土墙体裂缝识别方法. 智能建筑与智慧城市. 2022(11): 72-74 .