Classification of surface defects on steel sheet using convolutional neural networks
Open accessAbstract
A convolutional neural network (CNN) is proposed to learn multiple useful feature representations for a classification from low level (raw pixels) to high level (object).Convolutional kernels are initialized by the learned filter kernels that come from sparse auto-encoders.Unlike some traditional methods, which divide the feature abstracting and classifier training into two separated processes, a discriminative feature vector and a single multi-class classifier of softmax regression are learned simultaneously during the training process.Based on the learned high-quality feature representation, the classification can be efficiently performed.A real-world case of surface defects on steel sheet, which evaluates the classification performance of the proposed method, is depicted in detail.The experimental results indicate that the proposed method is quite simple, effective and robustness for the classification of surface defects on hot-rolled steel sheet.
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Because all local parts share weights, CNN does not take into account the difference of contribution to the whole between each local part.
“Because all local parts share weights, it does not take into account the difference of contribution to the whole between each local part.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [104]
Compared with a fully connected network, CNN reduces many parameters and simplifies calculation by adopting local connection, weight sharing and down sampling operations.
“Compare with fully connected network, it reduces many parameters and simplifies the calculation by adopting local connection, weight sharing and down sampling operations.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [104]
A CNN adopted as a classifier for defect classification of flat steel surfaces was found in experimental results to be powerful and robust for the classification task.
“In [104] and [105], a CNN was adopted as a classifier for the defect classification of flat steel surfaces, and the experimental results indicate that the CNN is powerful and robust for the classification task.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [104]
Deep learning methods such as CNN, GAN, and AutoEncoder have achieved outstanding performance in both feature learning and defect recognition for flat steel surfaces.
“Deep learning methods such as CNN, GAN, and AutoEncoder have nurtured many novel ideas that have achieved outstanding performance in both feature learning and defect recognition for flat steel surfaces.”
From Automated Visual Defect Classification for Flat Steel Surface: A Survey (2020) · cites this paper as [104]
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- Authors
- Zhou, S., Chen, Y., Zhang, D., Xie, J., & Zhou, Y.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Zhang, Dailin
- Venue
- Materiali in tehnologije
- Publisher
- Institute of Metals and Technology
- Published
- 2017-02-14
- Volume
- 51
- Issue
- 1
- Pages
- 123-131
- ISSN
- 1580-2949 (ISSN-L)
- 1580-3414
- OpenAlex
- W2591544917
- Cited by
- 130
- References
- 31
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 10 · 2023: 16 · 2022: 16 · 2021: 25 · 2020: 25 · 2019: 16 · 2018: 12 · 2017: 3
- FWCI
- 16.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Materiali in tehnologije (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (51174151)
- Topics
- 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
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- surface defect classification, steel sheet, hot-rolled steel, convolutional neural networks, sparse autoencoders, representation learning, softmax regression, image classification
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