과제정보
이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구입니다(No. 2022R1F1A1074663). 지원에 감사합니다.
참고문헌
- 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. https://doi.org/10.1016/j.autcon.2021.103989
- 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. https://doi.org/10.1109/TITS.2015.2477675
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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. https://doi.org/10.1177/1475921719900918
- 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. https://doi.org/10.1109/TITS.2016.2552248
- 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.
- 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. https://doi.org/10.1016/j.autcon.2021.103833
- 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. https://doi.org/10.1186/s40537-018-0162-3
- 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. https://doi.org/10.1016/j.eng.2018.11.030
- 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.
- 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. https://doi.org/10.1016/j.conbuildmat.2005.01.011
- 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. https://doi.org/10.3390/s20174849
- 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
- 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.
- 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.
- 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. https://doi.org/10.1016/j.patrec.2011.11.004