Cited by 345Industrial Vision Systems and Defect DetectionNon-Destructive Testing TechniquesAdvanced Neural Network Applications

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Steel defect classification with Max-Pooling Convolutional Neural Networks

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Masci, Jonathan, Meier, Ueli, Ciresan, Dan, Schmidhuber, Jurgen, Fricout, Gabriel·2012·The 2012 International Joint Conference on Neural Networks (IJCNN)

DOI 10.1109/ijcnn.2012.6252468

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Abstract

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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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)
DOI: 10.1109/ijcnn.2012.6252468
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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Steel defect classification with Max-Pooling Convolutional Neural Networks

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