Identification of Crack in Concrete Structures Based on MobileNetV2 of Lightweight Convolutional Network

FAN Lijun

doi:10.13204/j.gyjzG23021618

Volume 53 Issue 7

Jul. 2023

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Article Navigation > INDUSTRIAL CONSTRUCTION > 2023 > 53(7): 231-236

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

Citation:

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

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

Citation:

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

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Identification of Crack in Concrete Structures Based on MobileNetV2 of Lightweight Convolutional Network

doi: 10.13204/j.gyjzG23021618

FAN Lijun^1,2

1. China Railway Construction Group Co., Ltd., Beijing 100043, China;
2. Engineering Research Institute, China Railway Construction Group Co., Ltd., Beijing 100043, China

Received Date: 2023-02-16

Abstract

Abstract

With the increase of service time of concrete structures, the generated cracks will continue to expand and may cause damage to the structure. Therefore, crack detection is of great significance for the health monitoring of concrete structures, but traditional ultrasonic and artificial vision-based detection methods can not quickly classify cracks. Based on MobileNetV2 lightweight convolutional network and TensorFlow deep learning framework, a prediction model for rapid identification and classification of concrete structure crack was established. Firstly, the data set was extracted and partitioned based on the pathlib method; secondly, data enhancement based on transfer learning expanded the data set; thirdly, based on the TensorFlow framework, Keras was used to build the pooling layer of the convolutional network; finally, a complete convolutional network architecture and obtained the results were obtained. The test results showed that the model converged, the prediction accuracy reached 0.997 5, and the training time was only 710 s. It could provide help for mobile equipment detection in the project site.
- deep learning,
- convolutional network,
- concrete structures,
- MobileNetV2,
- crack

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References(18)

References

[1]	ZHANG L, YANG F, ZHANG Y D, et al. Road crack detection using deep convolutional neural network[C]//IEEE International Conference on Image Processing (ICIP). 2016:3708-3712.
[2]	DUNG C V, ANH L D. Autonomous concrete crack detection using deep fully convolutional neural network[J]. Automation in Construction, 2019, 99:52-58.
[3]	鲍跃全,李惠. 人工智能时代的土木工程[J]. 土木工程学报, 2019, 52(5):1-11.
[4]	ZHAO J, LI L. Research on image classification algorithm based on convolutional neural Network-TensorFlow[J/OL]. Journal of Physics,2010[2022-08-11].https://iopscience.iop.org/article/10.1088/1742-6596/2083/3/032054.
[5]	GU J, WANG Z, KUEN J, et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 2018,77:354-377, 3.
[6]	HE T, LIU Y, YU Y, et al. Application of deep convolutional neural network on feature extraction and detection of wood defects Çaǧlar Flrat Özgenel:Concrete Crack Images for Classification[J/OL]. Measurement, 2020[2023-02-11]. https://doi.org/10.1016/j.measurement.2019.107357.
[7]	LI W, FIELD K G, MORGAN D. Automated defect analysis in electron microscopic images[J]. NPJ Computational Materials, 2018(4):1-9.
[8]	DONG Y, SU C, QIAO P, et al. Microstructural crack segmentation of three-dimensional concrete images based on deep convolutional neural networks[J/OL]. Construction and Building Materials, 2020[2022-08-11].https://doi.org/10.1016/j.conbuildmat.2020.119185.
[9]	DAS A K, LEUNG C K Y, WAN K T. Application of deep convolutional neural networks for automated and rapid identification and computation of crack statistics of thin cracks in strain hardening cementitious composites (SHCCs)[J/OL]. Cement and Concrete Composites, 2021[2022-08-11].https://doi.org/10.1016/j.cemconcomp.2021.104159.
[10]	REZAIE A, ACHANTA R, GODIO M, et al. Comparison of crack segmentation using digital image correlation measurements and deep learning[J/OL]. Construction and Building Materials, 2020[2022-08-11].https://doi.org/10.1016/j.conbuildmat.2020.120474.
[11]	OH B K, PARK H S, GLISIC B. Prediction of long-term strain in concrete structure using convolutional neural networks, air temperature and time stamp of measurements[J/OL]. Automation in Construction, 2021[2022-08-11].https://doi.org/10.1016/j.autcon.
[12]	KHODABANDEHLOU H, PEKCAN G, FADALI M S. Vibration-based structural condition assessment using convolution neural networks[J/OL]. Structural Control and Health Monitoring,2018[2018-12-13].https://doi.org/10.1002/stc.2308.
[13]	WANG L, KAWAGUCHI K, WANG P. Damaged ceiling detection and localization in large-span structures using convolutional neural networks[J/OL]. Automation in Construction,2020[2022-08-11].https://doi.org/10.1016/j.autcon.2020.103230.
[14]	KUMAR N, RATHEE M, CHANDRAN N, et al. CrypTFlow:Secure TensorFlow inference[C]//Proceedings of the IEEE Symposium on Security and Privacy. 2020:336-353.
[15]	HOWARD A G, ZHU M, CHEN B, et al. MobileNets:efficient convolutional neural networks for mobile vision applications[EB/OL].[2022-08-11]. http://arxiv.org/abs/1704.04861.
[16]	ÖZGENEL F, GÖNENÇ S A. Performance comparison of pretrained convolutional neural networks on crack detection in buildings[C]//Proceedings of the ISARC 2018:35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon:The Future of Building Things. Berlin:2018.
[17]	SANDLER M, HOWARD A, ZHU M, et al. MobileNet V2:Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.New York:IEEE, 2018:4510-4520.
[18]	Çaǧlar Flrat Özgenel:Concrete crack images for classification[R/OL].Berlin:Orta Dogu Teknik Universitesi, 2019[2019-07-23]. https://data.mendeley.com/datasets/5y9wdsg2zt/2.