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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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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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
What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,603
Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons
Leung, T., & Malik, J. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011126920638 · Cited by 1,498 · cited 1×
“However, in a broader context the problem can be viewed as defect detection in textured material which has received considerable attention in computer vision [7], [8], [9].”
BackgroundRepresenting shape with a spatial pyramid kernel
Bosch, A., Zisserman, A., & Muñoz, X. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282340 · Cited by 1,423
(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229
Self-citationA Statistical Approach to Material Classification Using Image Patch Exemplars
Varma, M., & Zisserman, A. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.182 · Cited by 617
Convolutional Neural Network Committees for Handwritten Character Classification
Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2011) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2011.229 · Cited by 552
Self-citationDefect detection in textured materials using Gabor filters
Kumar, A., & Pang, G. K. H. (2002) · IEEE Transactions on Industry Applications · doi:10.1109/28.993164 · Cited by 475
Texture classification: are filter banks necessary?
Varma, M., & Zisserman, A. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211534 · Cited by 428
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
Ahonen, T., Matas, J., He, C., & Pietikäinen, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02230-2_7 · Cited by 417
A committee of neural networks for traffic sign classification
Ciresan, D. C., Meier, U., Masci, J., & Schmidhuber, J. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033458 · Cited by 387
Self-citationOn Random Weights and Unsupervised Feature Learning
Saxe, A., Koh, P. W., Chen, Z., Bhand, M., Suresh, B., & Ng, A. Y. (2011) · Cited by 353
Hierarchical Neural Networks for Image Interpretation
Behnke, S. (2003) · Lecture notes in computer science · doi:10.1007/b11963 · Cited by 227
Optimal Gabor filters for textile flaw detection
Bodnarova, A., Bennamoun, M., & Latham, S. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(02)00017-1 · Cited by 175
Face recognition approach based on rank correlation of Gabor-filtered images
Ayinde, O., & Yang, Y. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(01)00120-0 · Cited by 103
Martins, L. A. O., Padua, F. L. C., & Almeida, P. E. M. (2010) · IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society · doi:10.1109/iecon.2010.5675519 · Cited by 101
ICDAR 2011 - French Handwriting Recognition Competition
Grosicki, E., & Abed, H. E. (2011) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2011.290 · Cited by 101
Better Digit Recognition with a Committee of Simple Neural Nets
Meier, U., Ciresan, D. C., Gambardella, L. M., & Schmidhuber, J. (2011) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2011.252 · Cited by 72
Self-citationMonogenic-LBP: A new approach for rotation invariant texture classification
Zhang, L., Lei Zhang, A., Guo, Z., & Zhang, D. D. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5651885 · Cited by 68
Optimal Gabor Filters for Textile Flaw Detection
Bennamoun, M., Bodnarova, A., & Latham, S. (2002) · Faculty of Built Environment and Engineering · Cited by 14
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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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