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