Page 80 - Read Online

Ji et al. Intell Robot 2021;1(2):151-75 https://dx.doi.org/10.20517/ir.2021.14 Page 173

2017;39:640-51. DOI PubMed
55. Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation. Proceedings of 2015 IEEE International
Conference on Computer Vision (ICCV); 2015 Dec 7-13; Santiago, Chile. IEEE; 2015. p. 1520-8. DOI
56. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: semantic image segmentation with deep convolutional nets,
atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 2018;40:834-48. DOI PubMed
57. Liu W, Rabinovich A, Berg AC. Parsenet: looking wider to see better. arXiv preprint arXiv:1506.04579.
58. Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.
59. Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid scene parsing network. Proceedings of 2017 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR); 2017 Jul 21-26; Honolulu, HI, USA. IEEE; 2017. p. 6230-9. DOI
60. Fink O, Zio E, Weidmann U. Predicting component reliability and level of degradation with complex-valued neural networks.
Reliability Engineering & System Safety 2014;121:198-206. DOI
61. Giben X, Patel VM, Chellappa R. Material classification and semantic segmentation of railway track images with deep convolutional
neural networks. Proceedings of 2015 IEEE International Conference on Image Processing (ICIP); 2015 Sep 27-30; Quebec City, QC,
Canada. IEEE; 2015. p. 621-5. DOI
62. Faghih-Roohi S, Hajizadeh S, Núnez A, Babuska R, De Schutter B. Deep convolutional neural networks for detection of rail surface
defects. Proceedings of 2016 International joint conference on neural networks (IJCNN); 2016 Jul 24-29; Vancouver, BC, Canada.
IEEE; 2016. p. 2584-9. DOI
63. Bruin T, Verbert K, Babuska R. Railway track circuit fault diagnosis using recurrent neural networks. IEEE Trans Neural Netw Learn
Syst 2017;28:523-33. DOI PubMed
64. Gibert X, Patel VM, Chellappa R. Deep multitask learning for railway track inspection. IEEE Trans Intell Transport Syst
2017;18:153-64. DOI
65. Zhang X, Wang K, Wang Y, Shen Y, Hu H. An improved method of rail health monitoring based on CNN and multiple acoustic
emission events. Proceedings of 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC); 2017
May 22-25; Turin, Italy. IEEE; 2017. p. 1-6. DOI
66. Rao DJ, Mittal S, Ritika S. Siamese neural networks for one-shot detection of railway track switches. arXiv preprint
arXiv:1712.08036.
67. Mittal S, Rao D. Vision based railway track monitoring using deep learning. arXiv preprint arXiv:1711.06423. DOI
68. Santur Y, Karaköse M, Akin E. A new rail inspection method based on deep learning using laser cameras. Proceedings of 2017
International Artificial Intelligence and Data Processing Symposium (IDAP); 2017 Sep 16-17; Malatya, Turkey. IEEE; 2017. p. 1-6.
DOI
69. Santur Y, Karaköse M, Akin E. An adaptive fault diagnosis approach using pipeline implementation for railway inspection. Turk J
Elec Eng & Comp Sci 2018;26:987-98. DOI
70. Huang H, Xu J, Zhang J, Wu Q, Kirsch C. Railway infrastructure defects recognition using fine-grained deep convolutional neural
networks. Proceedings of 2018 Digital Image Computing: Techniques and Applications (DICTA); 2018 Dec 10-13; Canberra, ACT,
Australia. IEEE; 2018. p. 1-8. DOI
71. Zhang X, Zou Z, Wang K, et al. A new rail crack detection method using LSTM network for actual application based on AE
technology. Appl Acoust 2018;142:78-86. DOI
72. Ye T, Wang B, Song P, Li J. Automatic railway traffic object detection system using feature fusion refine neural network under
shunting mode. Sensors (Basel) 2018;18:1916. DOI
73. Kang G, Gao S, Yu L, Zhang D. Deep architecture for high-speed railway insulator surface defect detection: denoising autoencoder
with multitask learning. IEEE Trans Instrum Meas 2019;68:2679-90. DOI
74. Liang Z, Zhang H, Liu L, He Z, Zheng K. Defect detection of rail surface with deep convolutional neural networks. Proceedings of
2018 13th World Congress on Intelligent Control and Automation (WCICA); 2018 Jul 4-8; Changsha, China. IEEE; 2018. p. 1317-
22. DOI
75. Shang L, Yang Q, Wang J, Li S, Lei W. Detection of rail surface defects based on CNN image recognition and classification.
Proceedings of 2018 20th International Conference on Advanced Communication Technology (ICACT); 2018 Feb 11-14;
Chuncheon, Korea (South). IEEE; 2018. p. 45-51. DOI
76. Wang S, Dai P, Du X, Gu Z, Ma Y. Rail fastener automatic recognition method in complex background. Proceedings of Tenth
International Conference on Digital Image Processing (ICDIP 2018); 2018 Aug 9; Shanghai, China. International Society for Optics
and Photonics; 2018. p. 1080625. DOI
77. Yanan S, Hui Z, Li L, Hang Z. Rail surface defect detection method based on yolov3 deep learning networks. Proceedings of 2018
Chinese Automation Congress (CAC); 2018 Nov 30-Dec 2; Xi’an, China. IEEE; 2018. p. 1563-8.2. DOI
78. Xu X, Lei Y, Yang F. Railway subgrade defect automatic recognition method based on improved faster R-CNN. Sci Programming
2018;2018:1-12. DOI
79. Jamshidi A, Hajizadeh S, Su Z, et al. A decision support approach for condition-based maintenance of rails based on big data
analysis. Transp Res Part C Emerg Technol 2018;95:185-206. DOI
80. Bukhsh Z, Saeed A, Stipanovic I, Doree AG. Predictive maintenance using tree-based classification techniques: a case of railway
switches. Transp Res Part C Emerg Technol 2019;101:35-54. DOI
81. James A, Jie W, Xulei Y, et al. Tracknet - a deep learning based fault detection for railway track inspection. Proceedings of 2018
International Conference on Intelligent Rail Transportation (ICIRT); 2018 Dec 12-14; Singapore. IEEE; 2018. p. 1-5. DOI
82. Peng X, Jin X. Rail suspension system fault detection using deep semi-supervised feature extraction with one-class data.