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Flatness Defect Detection and Classification in Hot Rolled Steel Strips Using Convolutional Neural Networks

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11507))

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Abstract

This paper addresses the improvement of flatness defect detection and classification in the steel industry. Localization and classification of the defects is respectively taken care of by a detector and a classifier. The pipeline can start with either CSV or image files coming straight from the plant sensors. To probe the performance of the system, it was used to detect and classify flatness defects in hot steel strips. A total of about 513 strips produced in a real steelworks were used for this purpose for a total of about 4806 defect images. A comparison between different traditional machine learning and deep learning models was carried out showing better performances with the latter approach.

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Notes

  1. 1.

    In this work it is used with Mini-batches and momentum.

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Acknowledgments

The work described in the present paper was developed within the project entitled Integration of complex measurement information of thick products to optimise the through process geometry of hot rolled material for direct application INFOMAP (Contract No. RFSR-CT-2015-00008) that has received funding from the Research Fund for Coal and Steel of the European Union. The sole responsibility of the issues treated in the present paper lies with the authors; the Commission is not responsible for any use that may be made of the information contained therein.

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Authors and Affiliations

  1. TeCIP Institute, ICT-COISP Center, Scuola Superiore Sant’Anna, Via G. Moruzzi 1, 56124, Pisa, Italy

    Marco Vannocci, Antonio Ritacco, Angelo Castellano, Filippo Galli, Marco Vannucci, Vincenzo Iannino & Valentina Colla

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  1. Marco Vannocci
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  2. Antonio Ritacco
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  3. Angelo Castellano
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  4. Filippo Galli
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  5. Marco Vannucci
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  6. Vincenzo Iannino
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  7. Valentina Colla
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Corresponding author

Correspondence to Filippo Galli .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Vannocci, M. et al. (2019). Flatness Defect Detection and Classification in Hot Rolled Steel Strips Using Convolutional Neural Networks. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_19

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_19

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  • Online ISBN: 978-3-030-20518-8

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