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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“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]
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“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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Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Receptive fields and functional architecture of monkey striate cortex
Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,685
Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
Pattern Recognition and Machine Learning
Neal, R. M. (2007) · Technometrics · doi:10.1198/tech.2007.s518 · Cited by 4,629
Best practices for convolutional neural networks applied to visual document analysis
Simard, P. Y., Steinkraus, D., & Platt, J. C. (2005) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2003.1227801 · Cited by 2,862
Cited by
Sorted by citations
Segmentation-based deep-learning approach for surface-defect detection
Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01476-x · Cited by 996
State of the Art in Defect Detection Based on Machine Vision
Ren, Z., Fang, F., Yan, N., & Wu, Y. (2021) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-021-00343-6 · Cited by 896
Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network
Lv, X., Duan, F., Jiang, J.-J., Fu, X., & Gan, L. (2020) · Sensors · doi:10.3390/s20061562 · Cited by 643
Weimer, D., Scholz-Reiter, B., & Shpitalni, M. (2016) · CIRP Annals · doi:10.1016/j.cirp.2016.04.072 · Cited by 577
Picón, A., Álvarez-Gila, A., Seitz, M., Ortiz‐Barredo, A., Echazarra, J., & Johannes, A. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.04.002 · Cited by 621
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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