Cited by 338Infrastructure Maintenance and MonitoringIndustrial Vision Systems and Defect DetectionAdvanced Neural Network Applications

Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study

Wei, Xiukun, Yang, Ziming, Liu, Yuxin, Wei, Dehua, Jia, Limin, Li, Yujie·2019·Engineering Applications of Artificial Intelligence

Integrity signals

Registry

Crossref · 10.1016/j.engappai.2019.01.008

Cites

46 of 46 references verified0 citation errors · 0 not checkable

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  • Existing deep learning-based industrial defect detection methods do not specifically optimize the model's ability to extract and fuse features, which is crucial for defect detection.

    “However, the above deep learning-based industrial defect detection methods do not specifically optimize the model's ability to extract and fuse features, which is crucial for defect detection.”

    From A deep learning model for steel surface defect detection (2024) · cites this paper as [19]

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Authors
Wei, X., Yang, Z., Liu, Y., Wei, D., Jia, L., & Li, Y.
Affiliations
Beijing Jiaotong University (CN)
Corresponding author
Wei, Xiukun
Venue
Engineering Applications of Artificial Intelligence
Publisher
Elsevier BV
Published
2019-04
Volume
80
Pages
66-81
ISSN
0952-1976 (ISSN-L)
OpenAlex
W2912130719
Cited by
338
References
49
Citations by year
2026: 25 · 2025: 48 · 2024: 66 · 2023: 48 · 2022: 65 · 2021: 48 · 2020: 26 · 2019: 12
FWCI
28.24
Type
journal article
Language
en
Open access
closed
Locations
Engineering Applications of Artificial Intelligence (publishedVersion)
Funders
  • National Key R&D Program of China (2016YFB1200402)
  • State Key Lab of Rail Traffic Control and Safety (RCS2019ZT0046)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
railway track fasteners, VGG16, Faster R-CNN, track safety

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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