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Rail fastener defect inspection method for multi railways based on machine vision
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Junbo Liu, Yaping Huang, Shengchun Wang, Xinxin Zhao, Qi Zou, Xingyuan Zhang
Railway Sciences | 2022, 1(2) : 210 - 223
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Railway Sciences | 2022, 1(2): 210-223
Research paper
Rail fastener defect inspection method for multi railways based on machine vision
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Junbo Liu, Yaping Huang, Shengchun Wang, Xinxin Zhao, Qi Zou, Xingyuan Zhang
Affiliations
  • Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, China
  • Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China
  • Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, China
  • Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China
  • Beijing R&D Centre, Huawei Technologies Co Ltd, Shenzhen, China
Published: 2022-12-10 doi: 10.1108/RS-04-2022-0012
Outline
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Purpose

This research aims to improve the performance of rail fastener defect inspection method for multi railways, to effectively ensure the safety of railway operation.

Design/methodology/approach

Firstly, a fastener region location method based on online learning strategy was proposed, which can locate fastener regions according to the prior knowledge of track image and template matching method. Online learning strategy is used to update the template library dynamically, so that the method not only can locate fastener regions in the track images of multi railways, but also can automatically collect and annotate fastener samples. Secondly, a fastener defect recognition method based on deep convolutional neural network was proposed. The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region. The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.

Findings

Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways. Specifically, fastener location module has achieved an average detection rate of 99.36%, and fastener defect recognition module has achieved an average precision of 96.82%.

Originality/value

The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways, which has high reliability and strong adaptability to multi railways.

Rail fastener  /  Defects inspection  /  Multi railways  /  Image recognition  /  Deep convolutional neural network  /  Machine vision
Junbo Liu, Yaping Huang, Shengchun Wang, Xinxin Zhao, Qi Zou, Xingyuan Zhang. Rail fastener defect inspection method for multi railways based on machine vision[J]. Railway Sciences, 2022 , 1 (2) : 210 -223 . DOI: 10.1108/RS-04-2022-0012
  • the Key Research and Development Project of China Academy of Railway Sciences Corporation Limited(2021YJ310)
Year 2022 volume 1 Issue 2
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Article Info
doi: 10.1108/RS-04-2022-0012
  • Receive Date:2022-01-11
  • Online Date:2026-06-11
  • Published:2022-12-10
Article Data
Affiliations
History
  • Received:2022-01-11
  • Revised:2022-02-25
  • Accepted:2022-04-13
Funding
the Key Research and Development Project of China Academy of Railway Sciences Corporation Limited(2021YJ310)
Affiliations
    Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, China
    Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China
    Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited, Beijing, China
    Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, China
    Beijing R&D Centre, Huawei Technologies Co Ltd, Shenzhen, China

Corresponding:

Junbo Liu can be contacted at:
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表12种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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