A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads
Abstract
Discrete surface defects impact the riding quality and safety of a railway system. However, it is a challenge to inspect such defects in a vision system because of illumination inequality and the variation of reflection property of rail surfaces. This paper puts forward a real-time visual inspection system (VIS) for discrete surface defects. VIS first acquires a rail image by the image acquisition system, and then, it cuts the subimage of rail track by the track extraction algorithm. Subsequently, VIS enhances the contrast of the rail image using the local normalization (LN) method, which is nonlinear and illumination independent. At last, VIS detects defects using the defect localization based on projection profile (DLBP), which is robust to noise and very fast. Our experimental results demonstrate that VIS detects the Type-II defects with a recall of 93.10% and Type-I defects with a recall of 80.41%, and the proposed LN method and DLBP algorithm are better than the related well-established approaches. Furthermore, VIS is very fast with a linear computational time complexity, and it can be in real time to run on a 216-km/h test train under our experimental setup.
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LED is the most widely used luminaire in machine vision applications due to its advantages of longevity and low heat production.
“Among them, LED is the most widely used luminaire in machine vision applications due to its advantages of longevity and low heat production [35, 37, 38].”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [38]
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Chen, J., Liu, Z., Wang, H., Nunez, A., & Han, Z. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2017.2775345 · Cited by 456 · see citing works
Automated Visual Defect Classification for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148 · see citing works
Image data assessment approach for deep learning-based metal surface defect-detection systems
Lin, H.-I., & Wibowo, F. S. (2021) · IEEE Access · doi:10.1109/access.2021.3068256 · Cited by 43 · see citing works
- Authors
- Li, Q., & Ren, S.
- Affiliations
- Beijing Jiaotong University (CN)
- China Academy of Railway Sciences (CN)
- ORCID
- Li, Qingyong
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2012-08
- Volume
- 61
- Issue
- 8
- Pages
- 2189-2199
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W2069747077
- Cited by
- 299
- References
- 0
- Citations by year
- 2026: 8 · 2025: 23 · 2024: 30 · 2023: 27 · 2022: 28 · 2021: 38 · 2020: 28 · 2019: 24 · 2018: 25 · 2017: 20 · 2016: 21 · 2015: 16 · 2014: 5 · 2013: 5 · 2012: 1
- FWCI
- 21.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- real-time visual inspection, railway track inspection, local normalization, illumination invariance, image contrast enhancement, high-speed trains
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