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http://dx.doi.org/10.12815/kits.2022.21.5.171

CycleGAN Based Translation Method between Asphalt and Concrete Crack Images for Data Augmentation  

Shim, Seungbo (Korea Institute of Civil Engineering and Building Technology)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.21, no.5, 2022 , pp. 171-182 More about this Journal
Abstract
The safe use of a structure requires it to be maintained in an undamaged state. Thus, a typical factor that determines the safety of a structure is a crack in it. In addition, cracks are caused by various reasons, damage the structure in various ways, and exist in different shapes. Making matters worse, if these cracks are unattended, the risk of structural failure increases and proceeds to a catastrophe. Hence, recently, methods of checking structural damage using deep learning and computer vision technology have been introduced. These methods usually have the premise that there should be a large amount of training image data. However, the amount of training image data is always insufficient. Particularly, this insufficiency negatively affects the performance of deep learning crack detection algorithms. Hence, in this study, a method of augmenting crack image data based on the image translation technique was developed. In particular, this method obtained the crack image data for training a deep learning neural network model by transforming a specific case of a asphalt crack image into a concrete crack image or vice versa . Eventually, this method expected that a robust crack detection algorithm could be developed by increasing the diversity of its training data.
Keywords
CycleGAN; Image translation; Data augmentation; Asphalt crack; Concrete crack;
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1 Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N. and Shoaib, M. A.(2022), "Structural crack detection using deep convolutional neural networks", Automation in Construction, vol. 133, p.103989.   DOI
2 Krizhevsky, A., Sutskever, I. and Hinton, G. E.(2012), "Imagenet classification with deep convolutional neural networks", In Proceedings of Advances in Neural Information Processing Systems(NIPS), Montreal, Canada, vol. 25, pp.1097-1105.
3 Amhaz, R., Chambon, S., Idier, J. and Baltazart, V.(2016), "Automatic crack detection on two-dimensional pavement images: An algorithm based on minimal path selection", IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 10, pp.2718-2729.   DOI
4 Thanoon, W. A., Jaafar, M. S., Kadir, M. R. A. and Noorzaei, J.(2005), "Repair and structural performance of initially cracked reinforced concrete slabs", Construction and Building Materials, vol. 19, no. 8, pp.595-603.   DOI
5 Eisenbach, M., Stricker, R., Seichter, D., Amende, K., Debes, K., Sesselmann, M., Ebersbach, D., Stoeckert, U. and Gross, H. M.(2017), "How to get pavement distress detection ready for deep learning? A systematic approach", In Proceedings of International Joint Conference on Neural Networks(IJCNN), Anchorage, AK, USA, pp.2039-2047.
6 Zhang, L., Yang, F., Zhang, Y. D. and Zhu, Y. J.(2016), "Road crack detection using deep convolutional neural network", In Proceedings of IEEE International Conference on Image Processing(ICIP), Phoenix, AZ, USA, pp.3708-3712.
7 Karras, T., Laine, S. and Aila, T.(2019), "A style-based generator architecture for generative adversarial networks", In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Long Beach, CA, USA, pp.4401-4410.
8 Shi, Y., Cui, L., Qi, Z., Meng, F. and Chen, Z.(2016), "Automatic road crack detection using random structured forests", IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 12, pp.3434-3445.   DOI
9 Shim, S., Kim, J., Cho, G. C. and Lee, S. W.(2022), "Stereo-vision-based 3D concrete crack detection using adversarial learning with balanced ensemble discriminator networks", Structural Health Monitoring, 14759217221097868.
10 Spencer Jr, B. F., Hoskere, V. and Narazaki, Y.(2019), "Advances in computer vision-based civil infrastructure inspection and monitoring", Engineering, vol, 5, no. 2, pp.199-222.   DOI
11 Wang, Z., Yang, J., Jiang, H. and Fan, X.(2020), "CNN training with twenty samples for crack detection via data augmentation", Sensors, vol. 20, no. 17, p.4849.   DOI
12 Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y.(2014), "Generative adversarial nets", In Proceedings of Advances in Neural Information Processing Systems(NIPS), Montreal, Canada, pp.2672-2680.
13 Dellana, R. and Roy, K.(2016), "Data augmentation in CNN-based periocular authentication", In Proceedings of International Conference on Information Communication and Management(ICICM), Hatfield, UK, pp.141-145.
14 Shim, S., Kim, J., Lee, S. W. and Cho, G. C.(2021), "Road surface damage detection based on hierarchical architecture using lightweight auto-encoder network", Automation in Construction, vol. 130, p.103833.   DOI
15 Taylor, L. and Nitschke, G.(2018), "Improving deep learning with generic data augmentation", In Proc. IEEE Symposium Series on Computational Intelligence(SSCI), Bangalore, India, pp.1542-1547.
16 Zou, Q., Cao, Y., Li, Q., Mao, Q. and Wang, S.(2012), "CrackTree: Automatic crack detection from pavement images", Pattern Recognition Letters, vol. 33, no. 3, pp.227-238.   DOI
17 Isola, P., Zhu, J. Y., Zhou, T. and Efros, A. A.(2017), "Image-to-image translation with conditional adversarial networks", In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Honolulu, HI, USA, pp.1125-1134.
18 Long, L., Dohler, M. and Thons, S.(2022), "Determination of structural and damage detection system influencing parameters on the value of information", Structural Health Monitoring, vol. 21, no. 1, pp.19-36.   DOI
19 Xu, B., Wang, N., Chen, T. and Li, M.(2015), Empirical evaluation of rectified activations in convolutional network, arXiv:1505.00853 [Online]. Available at https://arxiv.org/abs/1505.00853
20 Zhu, J. Y., Park, T., Isola, P. and Efros, A. A.(2017), "Unpaired image-to-image translation using cycle-consistent adversarial networks", In Proceedings of the IEEE International Conference on Computer Vision(ICCV), Venice, Italy, pp.2223-2232.
21 Shorten, C. and Khoshgoftaar, T. M.(2019), "A survey on image data augmentation for deep learning", Journal of Big Data, vol. 6, no. 1, pp.1-48.   DOI