Steel defect classification with Max-Pooling Convolutional Neural Networks

Jonathan Masci, Ueli Meier, Dan Ciresan, Jürgen Schmidhuber, Gabriel Fricout

Research output: Chapter in Book/Report/Conference proceedingConference contribution

277 Scopus citations

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. © 2012 IEEE.
Original languageEnglish (US)
Title of host publicationProceedings of the International Joint Conference on Neural Networks
DOIs
StatePublished - Aug 22 2012
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2022-09-14

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