A comparison of deep convolutional neural networks for image-based detection of concrete surface cracks
Abstract
The aim of this paper is to compare the performance of four deep convolutional neural networks in the problem of image-based automated detection of concrete surface cracks in the case of a small dataset. This crack detection problem is treated as a binary classification problem, and it is solved by training a deep convolutional neural network on the small dataset. In this context, overfitting during training was the main issue to cope with and various techniques were applied to overcome this issue. The results of the experiments suggest that the best approach for this problem is to use the pretrained convolutional base of a large pretrained convolutional neural network as an automatic feature extraction method and adding a new binary classifier on top of the convolutional base. Then, at the training the new classifier and fine-tuning the last few layers of the pretrained network take place at the same time. The classification accuracy of the best deep convolutional neural network on the testing set is about 94%.
Keywords
deep convolutional neural network, pretrained network, image based crack detection, binary classification, overfitting,References
[1] Y.-J. Cha, W. Choi, O. B¨uy¨uk¨ozt¨urk. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5): 361–378, 2017.[2] F. Chollet. Deep Learning with Python. Manning Publications Co., 2018.
[3] S. Dorafshan, R.J. Thomas, M. Maguire. Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete. Construction and Building Materials, 186: 1031–1045, 2018.
[4] S. Dorafshan, R.J. Thomas, M. Maguire. SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks. Data in brief, 21: 1664–1668, 2018.
[5] C.V. Dung, L.D. Anh. Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, 99: 52–58, 2019.
[6] I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016.
[7] K. Gopalakrishnan. Deep learning in data-driven pavement image analysis and automated distress detection: A review. Data, 3(3): 28, 2018.
[8] L. Blier. A brief report of the Heuritech Deep Learing Meetup #5. hueritech Le Blog, 29.02.2016, https://blog.heuritech.com/2016/02/29/a-brief-report-of-the-heuritech-deep-learning-meetup-5/, accessed on 15.02.2019.
[9] B. Kim, S. Cho. Automated vision-based detection of cracks on concrete surfaces using a deep learning technique. Sensors, 18(10), 2018.
[10] H. Kim, E. Ahn, M. Shin, S.-H. Sim. Crack and noncrack classification from concrete surface images using machine learning. Structural Health Monitoring, 18(3): 725–738, 2019.
[11] C. Koch, K. Georgieva, V. Kasireddy, B. Akinci, P. Fieguth. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29(2): 196–210, 2015.
[12] A. Krizhevsky, I. Sutskever, G.E. Hinton. ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105, 2012.
[13] Y. LeCun. Generalization and network design strategies. In: R. Pfeifer, Z. Schreter, F. Fogelman, L. Steels [Eds], Connectionism in perspective. Elsevier, 1989.
[14] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521(7553): 436, 2015.
[15] S. Mallat. Understanding deep convolutional networks. Philosophical Transactions of the Royal Society of London Series A, 374, 16 pages, 2016.
[16] A. Mohan, S. Poobal. Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal, 57(2): 787–798, 2018.
[17] C¸ .F. ¨ Ozgenel, A.G. Sorgu¸c. Performance comparison of pretrained convolutional neural networks on crack detection in buildings. In: Proceedings of the International Symposium on Automation and Robotics in Construction, Vol. 35, pp. 1–8. IAARC Publications, 2018.
[18] W. Rawat, Z.Wang. Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation, 29(9): 2352–2449, 2017.
[19] W.R.L. Silva, D.S. Lucena. Concrete cracks detection based on deep learning image classification. In: Multidisciplinary Digital Publishing Institute Proceedings, Vol. 2, p. 489, 2018.
[20] K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
[21] B.F. Spencer, V. Hoskere, Y. Narazaki. Advances in computer vision-based civil infrastructure inspection and monitoring. Engineering, 5(2): 199–222, 2019.
[22] N. Srivastava, G.E. Hinton, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1): 1929–1958, 2014.
Published
Nov 15, 2019
How to Cite
SŁOŃSKI, Marek.
A comparison of deep convolutional neural networks for image-based detection of concrete surface cracks.
Computer Assisted Methods in Engineering and Science, [S.l.], v. 26, n. 2, p. 105-112, nov. 2019.
ISSN 2956-5839.
Available at: <https://cames.ippt.gov.pl/index.php/cames/article/view/267>. Date accessed: 23 nov. 2024.
doi: http://dx.doi.org/10.24423/cames.267.
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