Page 82 - Read Online

Ji et al. Intell Robot 2021;1(2):151-75 https://dx.doi.org/10.20517/ir.2021.14 Page 175

110. Liu Y, Sun X, Pang JHL. (, March). A YOLOv3-based deep learning application research for condition monitoring of rail thermite
welded joints. Proceedings of the 2020 2nd International Conference on Image, Video and Signal Processing; 2020 Mar; New York,
NY, USA. Association for Computing Machinery; 2020. p. 33-8. DOI
111. Aydin I, Akin E, Karakose M. Defect classification based on deep features for railway tracks in sustainable transportation. Appl Soft
Comput 2021;111:107706. DOI
112. Zheng D, Li L, Zheng S, et al. A defect detection method for rail surface and fasteners based on deep convolutional neural network.
Comput Intell Neurosci 2021;2021:2565500. DOI PubMed PMC
113. Wang W, Hu W, Wang W, et al. Automated crack severity level detection and classification for ballastless track slab using deep
convolutional neural network. Autom Constr 2021;124:103484. DOI
114. Chen Z, Wang Q, Yang K, et al. Deep learning for the detection and recognition of rail defects in ultrasound B-scan images. Transp
Res Rec 2021;2675:888-901. DOI
115. Liu J, Ma Z, Qiu Y, Ni X, Shi B, Liu H. Four discriminator cycle-consistent adversarial network for improving railway defective
fastener inspection. IEEE Trans Intell Transport Syst 2021. DOI
116. Wu Y, Qin Y, Qian Y, Guo F, Wang Z, Jia L. Hybrid deep learning architecture for rail surface segmentation and surface defect
detection. Computer aided Civil Eng 2022;37:227-44. DOI
117. Wan Z, Chen S. Railway tracks defects detection based on deep convolution neural networks. In: Liang Q, Wang W, Mu J, Liu X, Na
Z, Cai X, editors. Artificial intelligence in China. Singapore: Springer; 2021. p. 119-29. DOI
118. Ye T, Zhang X, Zhang Y, Liu J. Railway traffic object detection using differential feature fusion convolution neural network. IEEE
Trans Intell Transport Syst 2021;22:1375-87. DOI
119. Chen M, Zhai W, Zhu S, Xu L, Sun Y. Vibration-based damage detection of rail fastener using fully convolutional networks. Veh
Syst Dyn 2021. DOI
120. Tai JJ, Innocente MS, Mehmood O. FasteNet: a fast railway fastener detector. In: Yang X, Sherratt S, Dey N, Joshi A, editors.
Proceedings of Sixth International Congress on Information and Communication Technology. Singapore: Springer; 2022. p. 767-77.
DOI
121. Guo F, Qian Y, Rizos D, Suo Z, Chen X. Automatic rail surface defects inspection based on mask R-CNN. Transp Res Rec
2021;2675:655-68. DOI