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Volume 13
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10.3390/app13031904
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Article

Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network

by
Alexey N. Beskopylny
1,*,
Evgenii M. Shcherban’
2,
Sergey A. Stel’makh
3,
Levon R. Mailyan
3,
Besarion Meskhi
4,
Irina Razveeva
5,
Alexey Kozhakin
3,
Diana El’shaeva
3,
Nikita Beskopylny
6 and
Gleb Onore
5
1
Department of Transport Systems, Faculty of Roads and Transport Systems, Don State Technical University, 344003 Rostov-on-Don, Russia
2
Department of Engineering Geology, Bases, and Foundations, Don State Technical University, 344003 Rostov-on-Don, Russia
3
Department of Unique Buildings and Constructions Engineering, Don State Technical University, Gagarin Sq. 1, 344003 Rostov-on-Don, Russia
4
Department of Life Safety and Environmental Protection, Faculty of Life Safety and Environmental Engineering, Don State Technical University, 344003 Rostov-on-Don, Russia
5
Department of Mathematics and Informatics, Faculty of IT-Systems and Technology, Don State Technical University, Gagarin sqr., 1, 344003 Rostov-on-Don, Russia
6
Department Hardware and Software Engineering, Faculty of IT-Systems and Technology, Don State Technical University, 344003 Rostov-on-Don, Russia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(3), 1904; https://doi.org/10.3390/app13031904
Submission received: 8 January 2023 / Revised: 22 January 2023 / Accepted: 31 January 2023 / Published: 1 February 2023
(This article belongs to the Section Civil Engineering)
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Abstract

The creation and training of artificial neural networks with a given accuracy makes it possible to identify patterns and hidden relationships between physical and technological parameters in the production of unique building materials, predict mechanical properties, and solve the problem of detecting, classifying, and segmenting existing defects. The detection of defects of various kinds on elements of building materials at the primary stages of production can improve the quality of construction and identify the cause of particular damage. The technology for detecting cracks in building material samples is of great importance in building monitoring, in pre-venting the spread of defective material. In this paper, we consider the use of the YOLOv4 convolutional neural network for crack detection on building material samples. This was based on the creation of its own empirical database of images of samples of aerated concrete. The number of images was increased by applying our own augmentation algorithm. Optimization of the parameters of the intellectual model based on the YOLOv4 convolutional neural network was performed. Experimental results show that the YOLOv4 model developed in this article has high precision in defect detection problems: AP@50 = 85% and AP@75 = 68%. It should be noted that the model was trained on its own set of data obtained by simulating various shooting conditions, rotation angles, object deformations, and light distortions through image processing methods, which made it possible to apply the developed algorithm in practice.
Keywords: convolutional neural network; crack detection; data augmentation; image annotation; aerated concrete convolutional neural network; crack detection; data augmentation; image annotation; aerated concrete

Share and Cite

MDPI and ACS Style

Beskopylny, A.N.; Shcherban’, E.M.; Stel’makh, S.A.; Mailyan, L.R.; Meskhi, B.; Razveeva, I.; Kozhakin, A.; El’shaeva, D.; Beskopylny, N.; Onore, G. Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network. Appl. Sci. 2023, 13, 1904. https://doi.org/10.3390/app13031904

AMA Style

Beskopylny AN, Shcherban’ EM, Stel’makh SA, Mailyan LR, Meskhi B, Razveeva I, Kozhakin A, El’shaeva D, Beskopylny N, Onore G. Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network. Applied Sciences. 2023; 13(3):1904. https://doi.org/10.3390/app13031904

Chicago/Turabian Style

Beskopylny, Alexey N., Evgenii M. Shcherban’, Sergey A. Stel’makh, Levon R. Mailyan, Besarion Meskhi, Irina Razveeva, Alexey Kozhakin, Diana El’shaeva, Nikita Beskopylny, and Gleb Onore. 2023. "Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network" Applied Sciences 13, no. 3: 1904. https://doi.org/10.3390/app13031904

APA Style

Beskopylny, A. N., Shcherban’, E. M., Stel’makh, S. A., Mailyan, L. R., Meskhi, B., Razveeva, I., Kozhakin, A., El’shaeva, D., Beskopylny, N., & Onore, G. (2023). Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network. Applied Sciences, 13(3), 1904. https://doi.org/10.3390/app13031904

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