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DM-RIS: Deep multimodel rail inspection system with improved MRF-GMM and CNN

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成果类型:
期刊论文
作者:
Jin, Xiating*;Wang, Yaonan;Zhang, Hui*;Zhong, Hang;Liu, Li;...
通讯作者:
Jin, Xiating;Zhang, Hui
作者机构:
[Liu, Li; Zhong, Hang; Jin, Xiating; Wang, Yaonan] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China.
[Zhang, Hui] Changsha Univ Sci & Technol, Coll Elect & Informat Engn, Changsha 410012, Peoples R China.
[Zhang, Hui] Univ Windsor, Dept Elect & Comp Engn, CVSS Lab, Windsor, ON N9B 3P4, Canada.
[Wu, Q. M. Jonathan] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada.
[Yang, Yimin] Lakehead Univ, Dept Comp Sci, Thunder Bay, ON P7B 5E1, Canada.
通讯机构:
[Jin, Xiating] H
[Zhang, Hui] C
Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China.
Changsha Univ Sci & Technol, Coll Elect & Informat Engn, Changsha 410012, Peoples R China.
语种:
英文
关键词:
Faster RCNN;improved Gaussian mixture model (GMM);Markov random field (MRF);rail inspection;surface defect;visual detection
期刊:
IEEE Transactions on Instrumentation and Measurement
ISSN:
0018-9456
年:
2020
卷:
69
期:
4
页码:
1051-1065
基金类别:
Manuscript received October 28, 2018; revised March 19, 2019; accepted March 21, 2019. Date of publication April 16, 2019; date of current version March 10, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61601061, Grant 61841103, and Grant 81401490, in part by the National Key Research and Development Program of China under Grant 2018YFB1308200, in part by the Scientific Research Fund of Hunan Provincial Education Department under Grant 17C0046, in part by the Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing under Grant IRT2018009, and in part by the Hunan Key Project of Research and Development Plan under Grant 2018GK2022 and Grant 2018JJ3079. The Associate Editor coordinating the review process was Huang-Chen Lee. (Corresponding authors: Hui Zhang; Xiating Jin.) X. Jin, Y. Wang, H. Zhong, and L. Liu are with the College of Electrical and Information Engineering, Hunan University, Changsha 410082, China (e-mail: xtchin@hnu.edu.cn; yaonan@hnu.edu.cn; zhonghang@hnu.edu.cn; liuli@hnu.edu.cn).
机构署名:
本校为通讯机构
院系归属:
电气与信息工程学院
摘要:
Rail inspection system (RIS) remains an emergent instrumentation for railway transportation, with its capacity of measuring surface defect on steel rail. However, detecting technique and interpretation of RIS constitute a challenging problem since traditional technologies are expensive and prone to errors. In this paper, a deep multimodel RIS (DM-RIS) is established for surface defect where fast and robust spatially constrained Gaussian mixture model is presented for segmentation proposal and Faster RCNN is utilized for objective location in a parallel structure. First, we incorporate spatial ...

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