The DOI matches its Crossref record; 30 of the 31 references it cites were found in a registry or an index.
Steel defect classification with Max-Pooling Convolutional Neural Networks
Open accessAbstract
We present a Max-Pooling Convolutional Neural Network approach for supervised steel defect classification. On a classification task with 7 defects, collected from a real production line, an error rate of 7% is obtained. Compared to SVM classifiers trained on commonly used feature descriptors our best net performs at least two times better. Not only we do obtain much better results, but the proposed method also works directly on raw pixel intensities of detected and segmented steel defects, avoiding further time consuming and hard to optimize ad-hoc preprocessing.
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In the citing paper
“Steel surfaces exhibit many types of defects, and NNBP has been extensively used to classify such defects [31,36,54,59,63,72,78,80].”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [80]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“With 7% error rate, MPCNN performed much better than SVM for seven defects in cold strips.”
From Review of vision-based steel surface inspection systems (2014) · cites this paper as [80]
Experimental — accuracy not yet measuredIn the citing paper
“For deep learning detection algorithms (such as M-Pooling CNN [17], Improved YOLO [19]), their detection accuracy increases with data volume and their real-time performance is better than that of the traditional algorithms.”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [17]
In the citing paper
“Masci J. [7] 150x150 6.2 ms 90% 7”
From Real-time system for automatic cold strip surface defect detection (2019) · cites this paper as [7]
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Citations
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Metadata
- Authors
- Masci, J., Meier, U., Ciresan, D., Schmidhuber, J., & Fricout, G.
- Affiliations
- University of Applied Sciences and Arts of Southern Switzerland (CH)
- ORCID
- Masci, Jonathan
- Venue
- The 2012 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 1-6
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W1981759979
- Cited by
- 345
- References
- 31
- Citations by year
- 2026: 5 · 2025: 28 · 2024: 36 · 2023: 26 · 2022: 39 · 2021: 54 · 2020: 52 · 2019: 47 · 2018: 28 · 2017: 11 · 2016: 8 · 2015: 2 · 2014: 3 · 2013: 5 · 2012: 1
- FWCI
- 8.80
- Type
- conference paper
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- public-domain
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, public-domain)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- max pooling, convolutional neural networks, defect detection, defect segmentation
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