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Semantic Segmentation Based on Convolution Neural Network for Steel Strip Position Estimation

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference (EANN 2020)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 2))

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Abstract

In this paper, a method to access the location of a steel strip in the rolling process was developed. The method consists of a hybrid system composed of a CNN-based semantic segmentation followed by morphological operation and outlier removal. The proposed method was capable of estimating the position of the strip with high precision and low computational burden, making it suitable for the application. The implementation of automatic estimation for the steel strip positioning, replacing the current human operation, can yield substantial costs saving. Future work will be carried out for the and integration of automatic control in the process.

Supported by Budapest University of Technology and Economics.

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Acknowledgment

The research reported in this paper was supported by the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Artificial Intelligence research area of Budapest University of Technology and Economics (BME FIKP-MI/FM); by the National Research, Development and Innovation Fund (TUDFO/51757/2019-ITM, Thematic Excellence Program); by the János Bolyai Scholarship of the Hungarian Academy of Sciences to BVN; and by the Stipendium Hungaricum Scholarship Programme.

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Authors and Affiliations

  1. Department of Mechatronics, Optics and Mechanical Engineering Informatics, Budapest University of Technology and Economics, Budapest, Hungary

    Aline de Faria Lemos & Bálazs Vince Nagy

Authors
  1. Aline de Faria Lemos
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  2. Bálazs Vince Nagy
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Correspondence to Aline de Faria Lemos .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Lancaster University, Lancaster, UK

    Plamen Parvanov Angelov

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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de Faria Lemos, A., Nagy, B.V. (2020). Semantic Segmentation Based on Convolution Neural Network for Steel Strip Position Estimation. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_13

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  • DOI: https://doi.org/10.1007/978-3-030-48791-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48790-4

  • Online ISBN: 978-3-030-48791-1

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