A classification model of railway fasteners based on computer vision
Author:
Funder
Sichuan Province Science and Technology Support Program
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
http://link.springer.com/content/pdf/10.1007/s00521-019-04337-z.pdf
Reference27 articles.
1. Wei JH, Liu C, Ren TQ, Liu HX, Zhou WJ (2017) Online condition monitoring of a rail fastening system on high-speed railways based on wavelet packet analysis. Sensors 17(2):318
2. Mao QZ, Cui H, Hu QW, Ren XC (2018) A rigorous fastener inspection approach for high-speed railway from structure light sensors. ISPRS J Photogramm Remote Sens 143:249–267
3. Wang L, Zhang B, Chen XA (2011) Inspection system for loss of rail fastening nut based on computer vision. Comput Eng Des 32(12):4147–4150
4. Yang JF, Tao W, Liu MH, Zhang YJ, Zhang HB, Zhao H (2011) An efficient direction field-based method for the detection of fasteners on high-speed railways. Sensors 11(8):7364–7381
5. Dou YG, Huang YP, Li QY, Luo SW (2014) A fast template matching-based algorithm for railway bolts detection. Int J Mach Learn Cybernet 5(6):835–844
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