A New Ensemble Approach based on Deep Convolutional Neural Networks for Steel Surface Defect classification
Open accessAbstract
Steel surface defect recognition is a crucial component of automated steel surface inspection system which influences the quality of steel greatly. To improve the accuracy rate, an ensemble approach that integrating different deep convolutional neural networks (DCNNs) is proposed in this paper. Firstly, three different DCNNs are trained respectively with data augmentation to reduce over-fitting. Various optimization methods and tricks are used to reduce the error in the training procedure. Secondly, three well-trained models are combined. The experimental results show that the proposed approach made a state-of-art performance on accuracy rate and robustness in steel surface defect classification.
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WRN has massive computation cost during training, as long as 2 days, even if the input images were resized to 32×32.
“However, as shown in the Table 4, WRN has so many weights to train that results in a massive computation cost during training, as long as 2 days as their report, even if the input images were resized to 32×32.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [56]
WRN-28-20 gets the best-reported result at 99.89% on the NEU dataset.
“the WRN-28-20 gets the best-reported result at 99.89%.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [56]
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Recent advances in surface defect inspection of industrial products using deep learning techniques
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A Compact Convolutional Neural Network for Surface Defect Inspection
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- Authors
- Chen, W., Gao, Y., Gao, L., & Li, X.
- Affiliations
- State Key Laboratory of Digital Manufacturing Equipment and Technology (CN)
- Corresponding author
- Li, Xinyu
- Venue
- Procedia CIRP
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 72
- Pages
- 1069-1072
- ISSN
- 2212-8271 (ISSN-L)
- OpenAlex
- W2809788830
- Cited by
- 56
- References
- 20
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 7 · 2023: 8 · 2022: 11 · 2021: 13 · 2020: 5 · 2019: 4
- FWCI
- 4.54
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia CIRP (publishedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China (51775216, 51711530038, 51435009, B16019)
- Higher Education Discipline Innovation Project (111Project, B16019)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- ensemble learning, deep convolutional neural networks, data augmentation, overfitting reduction, model combination, defect detection
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