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Link to original content: https://doi.org/10.5220/0008944602460252
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Authors: Junesuk Lee 1 ; Geon-Tae Ahn 2 ; Byoung-Ju Yun 1 and Soon-Yong Park 1

Affiliations: 1 School of Electronics Engineering, Kyungpook National University, Daegu, South Korea ; 2 Research Institute of Industrial Science and Technology, Pohang, South Korea

Keyword(s): Path Estimation, Deep Learning, Intelligent Robots, Industrial Robots.

Abstract: In the steel manufacturing process, de-slagging machine is used to remove slag floating on molten metal in a ladle. In general, temperature of floating slag on the surface of the molten metal is above 1,500℃. The process of removing such slag at high temperatures is dangerous and is only performed by trained human operators. In this paper, we propose a deep learning method for estimating the slag removal path to automate slag removal task. We propose an idea of developing a slag distribution image structure(SDIS); combined with a deep learning model to estimate the removal path in an environment in which the flow of molten metal cannot be controlled. The SDIS is given as the input into to the proposed deep learning model, which we train by imitating the removal task of experienced operators. We use both quantitative and qualitative analyses to evaluate the accuracy of the proposed method with the experienced operators.

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Paper citation in several formats:
Lee, J. ; Ahn, G. ; Yun, B. and Park, S. (2020). Slag Removal Path Estimation by Slag Distribution and Deep Learning. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 246-252. DOI: 10.5220/0008944602460252

@conference{visapp20,
author={Junesuk Lee and Geon{-}Tae Ahn and Byoung{-}Ju Yun and Soon{-}Yong Park},
title={Slag Removal Path Estimation by Slag Distribution and Deep Learning},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={246-252},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008944602460252},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Slag Removal Path Estimation by Slag Distribution and Deep Learning
SN - 978-989-758-402-2
IS - 2184-4321
AU - Lee, J.
AU - Ahn, G.
AU - Yun, B.
AU - Park, S.
PY - 2020
SP - 246
EP - 252
DO - 10.5220/0008944602460252
PB - SciTePress

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