[1] Li S, Zhao X. Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique. Adv Civ Eng 2019;2019:1–12. doi:10.1155/2019/6520620.
[2] Yang X, Li H, Yu Y, Luo X, Huang T, Yang X. Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network. Comput Civ Infrastruct Eng 2018;33:1090–109. doi:10.1111/mice.12412.
[3] Mikołajczyk A, Grochowski M. Data augmentation for improving deep learning in image classification problem. 2018 Int Interdiscip PhD Work, IEEE; 2018, p. 117–22.
[4] Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning. ArXiv Prepr ArXiv171204621 2017.
[5] Inoue H. Data augmentation by pairing samples for images classification. ArXiv Prepr ArXiv180102929 2018.
[6] Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. Adv Neural Inf Process Syst, 2014, p. 2672–80.
[7] Wang C, Xu C, Yao X, Tao D. Evolutionary Generative Adversarial Networks. IEEE Trans Evol Comput 2019;23:921–34. doi:10.1109/TEVC.2019.2895748.
[8] Yang W, Zhang X, Tian Y, Wang W, Xue J-H, Liao Q. Deep Learning for Single Image Super-Resolution: A Brief Review. IEEE Trans Multimed 2019;21:3106–21. doi:10.1109/TMM.2019.2919431.
[9] Hoang N-D. Detection of Surface Crack in Building Structures Using Image Processing Technique with an Improved Otsu Method for Image Thresholding. Adv Civ Eng 2018;2018:1–10. doi:10.1155/2018/3924120.
[10] Wang M, Chen Z, Wu QMJ, Jian M. Improved face super-resolution generative adversarial networks. Mach Vis Appl 2020;31:22. doi:10.1007/s00138-020-01073-6.
[11] Zhang T. Research and Improvement of Single Image Super-Resolution Based on Generative Adversarial Network. J Phys Conf Ser, vol. 1237, IOP Publishing; 2019, p. 32046.
[12] Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. ArXiv Prepr ArXiv14091556 2014.
[13] Mikami H, Suganuma H, Tanaka Y, Kageyama Y. Imagenet/resnet-50 training in 224 seconds. ArXiv Prepr ArXiv181105233 2018:1–8.
[14] Dhankhar P. ResNet-50 and VGG-16 for recognizing Facial Emotions. Int J Innov Eng Technol 2019;13:126–30.
[15] Panella F, Boehm J, Loo Y, Kaushik A, Gonzalez D. Deep learning and image processing for automated crack detection and defect measurement in underground structures. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci 2018;XLII–2:829–35. doi:10.5194/isprs-archives-XLII-2-829-2018.
[16] Nandepu R. Understanding and implementation of Residual Networks (ResNets) 2019. https://medium.com/analytics-vidhya/understanding-and-implementation-of-residual-networks-resnets-b80f9a507b9c (accessed October 31, 2019).
[17] He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. 2016 IEEE Conf Comput Vis Pattern Recognit, IEEE; 2016, p. 770–8. doi:10.1109/CVPR.2016.90.
[18] Zhang K, Cheng H-D, Gai S. Efficient Dense-Dilation Network for Pavement Cracks Detection with Large Input Image Size. 2018 21st Int Conf Intell Transp Syst, IEEE; 2018, p. 884–9. doi:10.1109/ITSC.2018.8569958.
[19] Zhang K, Cheng HD, Zhang B. Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning. J Comput Civ Eng 2018;32:04018001. doi:10.1061/(ASCE)CP.1943-5487.0000736.
[20] Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, et al. Photo-realistic single image super-resolution using a generative adversarial network. Proc IEEE Conf Comput Vis pattern Recognit, 2017, p. 4681–90.
[21] Zhu X, Zhang L, Zhang L, Liu X, Shen Y, Zhao S. GAN-Based Image Super-Resolution with a Novel Quality Loss. Math Probl Eng 2020;2020:1–12. doi:10.1155/2020/5217429.
[22] Cha Y-J, Choi W, Büyüköztürk O. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks. Comput Civ Infrastruct Eng 2017;32:361–78. doi:10.1111/mice.12263.
[23] Sultana F, Sufian A, Dutta P. Advancements in Image Classification using Convolutional Neural Network. 2018 Fourth Int Conf Res Comput Intell Commun Networks, IEEE; 2018, p. 122–9. doi:10.1109/ICRCICN.2018.8718718.
[24] Zhang L, Yang F, Daniel Zhang Y, Zhu YJ. Road crack detection using deep convolutional neural network. 2016 IEEE Int Conf Image Process, IEEE; 2016, p. 3708–12. doi:10.1109/ICIP.2016.7533052.