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Authors: Natália F. De C. Meira 1 ; Mateus C. Silva 2 ; Andrea G. C. Bianchi 2 ; Cláudio B. Vieira 1 ; Alinne Souza 3 ; Efrem Ribeiro 3 ; Roberto O. Junior 3 and Ricardo A. R. Oliveira 2

Affiliations: 1 Metallurgical Engineering Department, Federal University of Ouro Preto, Ouro Preto, Brazil ; 2 Department of Computer Science, Federal University of Ouro Preto, Ouro Preto, Brazil ; 3 ArcelorMittal, João Monlevade, Brazil

Keyword(s): Convolutional Neural Network, Segmentation, Mask R-CNN, Steel Industry.

Abstract: Particle size is an important quality parameter for raw materials in steel industry processes. In this work, we propose to implement the Mask-R-CNN algorithm to segment quasi-particles by size classes. We created a dataset with real images of an industrial environment, labeled the quasi-particles by size classes, and performed four training sessions by adjusting the model’s hyperparameters. The results indicated that the model segments with well-defined edges and applications as classes correctly. We obtained a mAP between 0.2333 and 0.2585. Additionally, hit and detection rates increase for larger particle size classes.

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Paper citation in several formats:
Meira, N.; Silva, M.; Bianchi, A.; Vieira, C.; Souza, A.; Ribeiro, E.; O. Junior, R. and Oliveira, R. (2022). Mask R-CNN Applied to Quasi-particle Segmentation from the Hybrid Pelletized Sinter (HPS) Process. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 462-469. DOI: 10.5220/0010836900003124

@conference{visapp22,
author={Natália F. De C. Meira. and Mateus C. Silva. and Andrea G. C. Bianchi. and Cláudio B. Vieira. and Alinne Souza. and Efrem Ribeiro. and Roberto {O. Junior}. and Ricardo A. R. Oliveira.},
title={Mask R-CNN Applied to Quasi-particle Segmentation from the Hybrid Pelletized Sinter (HPS) Process},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={462-469},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010836900003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Mask R-CNN Applied to Quasi-particle Segmentation from the Hybrid Pelletized Sinter (HPS) Process
SN - 978-989-758-555-5
IS - 2184-4321
AU - Meira, N.
AU - Silva, M.
AU - Bianchi, A.
AU - Vieira, C.
AU - Souza, A.
AU - Ribeiro, E.
AU - O. Junior, R.
AU - Oliveira, R.
PY - 2022
SP - 462
EP - 469
DO - 10.5220/0010836900003124
PB - SciTePress