Automatic defect detection of fasteners on the catenary support device using deep convolutional neural network
Open accessAbstract
The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners' defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan-Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments.
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A three stage cascaded detection architecture for fasteners on catenary support device yielded encouraging results and can reduce human labour needed for maintenance and enable processing lots of data accurately.
“The results are encouraging and thus, present the opportunity to reduce the human labour needed for maintenance and enable the system to process lots of data accurately.”
From A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2022) · cites this paper as [45]
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“Chen et al. [29] proposed a method based on the deep convolutional neural networks to detect the defect of the fasteners.”
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- Authors
- Chen, J., Liu, Z., Wang, H., Nunez, A., & Han, Z.
- Affiliations
- Southwest Jiaotong University (CN)
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2018-02
- Volume
- 67
- Issue
- 2
- Pages
- 257-269
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W2772386856
- Cited by
- 456
- References
- 39
- Citations by year
- 2026: 22 · 2025: 44 · 2024: 44 · 2023: 72 · 2022: 73 · 2021: 77 · 2020: 68 · 2019: 40 · 2018: 15 · 2017: 1
- FWCI
- 35.47
- Type
- journal article
- Language
- en
- Open access
- green
- resolver.tudelft.nl
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Research Repository (Delft University of Technology) (submittedVersion)
- Funders
- National Natural Science Foundation of China (U1434203, 51377136, 51407147)
- Sichuan Province Youth Science and Technology Innovation Team (2016TD0012)
- 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 › Engineering › Mechanical Engineering › Power Line Inspection Robots
- Keywords
- deep convolutional neural networks, image-based inspection, coarse-to-fine detection, high-speed rail, automated visual inspection
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