References
- Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M. and Inman, D. J. (2017). "Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks." Journal of Sound and Vibration, Elsevier, Vol. 388, pp. 154-170, https://doi.org/10.1016/j.jsv.2016.10.043.
- Cha, Y. J., Choi, W. and Buyukozturk, O. (2017). "Deep learning-based crack damage detection using convolutional neural network." Computer-Aided Civil and Infrastructure Engineering, Wiley, Vol. 32, No. 5, pp. 361-378, https://doi.org/10.1111/mice.12263.
- Gao, Y. and Mosalam, K. M. (2018). "Deep transfer learning for image-based structural damage recognition." Computer-Aided Civil and Infrastructure Engineering, Vol. 33, No. 9, pp. 748-768. https://doi.org/10.1111/mice.12363
- Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q. V. and Adam, H. (2019). "Searching for MobileNetV3." arXiv preprint, https://arxiv.org/abs/1905.02244v5.
- Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M. and Adam, H. (2017). "MobileNets : efficient convolutional neural networks for mobile vision applications." arXiv preprint, https://arxiv.org/abs/1704.04861v1.
- Kingma, D. P. and Ba, J. (2014). "ADAM: A method for stochastic optimization." Proceedings of 3rd International Conference for Learning Representations, San Diego, USA, 2015, arXiv preprint, https://arxiv.org/abs/1412.6980.
- Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012). "Imagenet classification with deep convolutional neural networks." Advances in Neural Information Processing Systems, MIT Press, Vol. 5, pp. 1097-1105.
- Kurbiel, T. and Khaleghian, S. (2017). "Training of deep neural networks based on distance measures using RMSProp." arXiv preprint, https://arxiv.org/abs/1708.01911.
- Lin, Y., Nie, Z. and Ma, H. (2017). "Structural damage detection with automatic feature-extraction through deep learning." Computer-Aided Civil and Infrastructure Engineering, Wiley, Vol. 32, No. 12, pp. 1025-1046, https://doi.org/10.1111/mice.12313.
- Nam, W. S., Jung, H., Park, K. H., Kim, C. M. and Kim, G. S. (2022). "Development of deep learning-based damage detection prototype for concrete bridge condition evaluation." KSCE Journal of Civil and Environmental Engineering Research, KSCE, Vol. 42, No. 1, pp. 107-116, https://doi.org/10.12652/Ksce.2022.42.1.0107 (in Korean).
- Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. and Chen, L.-C. (2018). "Mobilenetv2 : Inverted residuals and linear bottlenecks." https://arxiv.org/abs/1801.04381.
- Scherer, D., Muller, A. and Behnke, S. (2010). "Evaluation of pooling operations in convolutional architectures for object recognition." Proceedings of 20th International Conference on Artificial Neural Networks (ICANN), Thessaloniki, Greece, pp. 92-101.
- Sifre, L. (2014). Rigid-motion scattering for image classification. PhD thesis, Ecole Polytechnique, CMAP, New York.
- Soukup, D. and Huber-Mork, R. (2014). "Convolutional neural networks for steel surface defect detection from photometric stereo images." Proceedings of 10th International Symposium on Visual Computing, Las Vegas, NV, pp. 668-677.
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014). "Dropout : A simple way to prevent neural networks from overfitting." Journal of Machine Learning Research, JMLR.org, Vol. 15, No. 1, pp. 1929-1958.
- Vetrivel, A., Gerke, M., Kerl, N., Nex, F. and Vosselman, G. (2017). "Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images and multiple-kernel-learning." ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, Vol. 140, pp. 45-59, https://doi.org/10.1016/j.isprsjprs.2017.03.001.
- Yeum, C. M. and Dyke, S. J. (2015). "Vision-based automated crack detection for bridge inspection." Computer-Aided Civil and Infrastructure Engineering, Wiley, Vol. 30, No. 10, pp. 759-770, https://doi.org/10.1111/mice.12141.