[1] Iranian Office of the Deputy for Technical Affairs Bureau of Technical Affairs and Standards, Iranian structural welding code No. 228, Tehran, Center for scientific documents and publications, (2001), (In Persian).
[2] Iranian Office of National Regulations and Building Control, Guide to welding and welded joints in steel buildings, Tehran, Tosseh publisher, (2015), (In Persian).
[3] Fatemi S.M., Visual inspection of welding, Tehran, Keyfiat publisher, (2018), (In Persian).
[4] Mahmoudi, A., & Regragui, F., "Welding defect detection by segmentation of radiographic images". 2009 WRI World Congress on Computer Science and Information Engineering, Vol. 7, pp. 111-115. IEEE. (2009, March).
[5] Sun, Y., Bai, P., Sun, H. Y., & Zhou, P., "Real-time automatic detection of weld defects in steel pipe", Ndt & E International, 38(7), 522-528, (2005).
[6] Y.J. Cha, W. Choi, G. Suh, S. Mahmoudkhani, O. Büyüköztürk, "Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types", Computer‐Aided Civil and Infrastructure Engineering, 33(9), pp. 731-747, (2018).
[7] A. Krizhevsky, I. Sutskever, G.E. Hinton, "Imagenet classification with deep convolutional neural networks", Advances in neural information processing systems, 25, pp. 1097-1105, (2012).
[8] Y. LeCun, Y. Bengio, "Convolutional networks for images, speech and time series", The handbook of brain theory and neural networks, 3361(10), 1995, (1995).
[9] Y.J. Cha, W. Choi, O. Büyüköztürk, "Deep learning‐based crack damage detection using convolutional neural networks", Computer‐Aided Civil and Infrastructure Engineering, 32(5), pp. 361-378, (2017).
[10] M. Mohtasham Khani, S. Vahidnia, L. Ghasemzadeh, Y.E. Ozturk, M. Yuvalaklioglu, S. Akin, N.K. Ure, "Deep-learning-based crack detection with applications for the structural health monitoring of gas turbines", Structural Health Monitoring, 19(5), pp. 1440-1452, (2020).
[11] C. Feng, H. Zhang, S. Wang, Y. Li, H. Wang, F. Yan, "Structural damage detection using deep convolutional neural network and transfer learning", KSCE Journal of Civil Engineering, 23(10), pp. 4493-4502, (2019).
[12] J. Long, E. Shelhamer, T. Darrell, "Fully convolutional networks for semantic segmentation", IEEE conference on computer vision and pattern recognition, pp. 3431-3440, (2015).
[13] X. Yang, H. Li, Y. Yu, X. Luo, T. Huang, X. Yang, "Automatic pixel‐level crack detection and measurement using fully convolutional network", Computer‐Aided Civil and Infrastructure Engineering, 33(12), 1090, (2018).
[14] L. Zhang, F. Yang, Y.D. Zhang, Y.J. Zhu, "Road crack detection using deep convolutional neural network", 2016 IEEE international conference on image processing (ICIP), IEEE, 2016, pp. 3708-3712.
[15] V. Hoskere, Y. Narazaki, T.A. Hoang, B. Spencer Jr, "MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure", Journal of Civil Structural Health Monitoring, 10, 757-773, (2020).
[16] O. Ronneberger, P. Fischer, T. Brox, "Unet: Convolutional networks for biomedical image segmentation", International Conference on Medical image computing and computerassisted intervention, Springer, pp. 234, (2015).
[17] Z. Liu, Y. Cao, Y. Wang, W. Wang, "Computer vision-based concrete crack detection using U-net fully convolutional networks", Automation in Construction, 104, pp. 129-139, (2019).
[18] L.C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A.L. Yuille, "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution and fully connected crfs", IEEE transactions on pattern analysis and machine intelligence, 40(4), pp. 834-848, (2017).
[19] L.-C. Chen, G. Papandreou, F. Schroff, H. Adam, "Rethinking atrous convolution for semantic image segmentation", arXiv preprint arXiv,1706.05587, (2017).
[20] L.C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, "Encoder-decoder with atrous separable convolution for semantic image segmentation", the European conference on computer vision (ECCV), pp. 801-818, (2018).
[21] Zhang, Z., Wen, G., & Chen, S., "Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding", Journal of Manufacturing Processes, 45, 208-216, (2019).
[22] Feng, S., Zhou, H., & Dong, H., "Using deep neural network with small dataset to predict material defects", Materials & Design, 162, 300-310, (2019).
[23] B. Zoph, V. Vasudevan, J. Shlens, Q.V. Le, "Learning transferable architectures for scalable image recognition", IEEE conference on computer vision and pattern recognition, pp. 8697-8710, (2018).
[24] Zhang, Y., You, D., Gao, X., Zhang, N., & Gao, P. P., "Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of thick plates", Journal of Manufacturing Systems, 51, 87-94, (2019).
[25] Chollet, F., "Deep Learning with Python", United States: Manning Publications, (2018).
[26] Hou, W., Wei, Y., Guo, J., & Jin, Y., "Automatic detection of welding defects using deep neural network" Journal of Physics: Conference Series, Vol. 933, No. 1, p. 012006, IOP Publishing, (2017, December).
[27] Deng, L., "A tutorial survey of architectures, algorithms, and applications for deep learning", APSIPA Transactions on Signal and Information Processing, (2014).
ارسال نظر در مورد این مقاله