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Authors: Flávio Cardoso 1 ; 2 ; Mateus Silva 3 ; Natália Meira 3 ; Ricardo Oliveira 3 ; 1 and Andrea Bianchi 1 ; 3

Affiliations: 1 Graduate Program in Instrumentation, Control and Automation of Mining Processes, Instituto Tecnológico Vale, Federal University of Ouro Preto, Ouro Preto, Brazil ; 2 VALE S.A., Nova Lima, Brazil ; 3 Department of Computer Science, Federal University of Ouro Preto, Ouro Preto, Brazil

Keyword(s): Edge AI, Particle Size Detection, Cloudlets, Mask R-CNN.

Abstract: Monitoring and controlling the particle size is essential to reducing the variability and optimizing energy efficiency in mineral process plants. The industry standard utilizes laboratory processes for particle size characterization; the problems that arise here are obtaining representative sample from the bulk and finding a rapid method of particle size assessment. We propose a machine vision concept based on Edge AI architecture and deep convolutional neural algorithms to enable a real-time analysis of particle size, as an alternative to offline laboratory process. The present paper is part of this proposed concept and aims exclusively to validate a deep convolutional neural network algorithm trained from synthetic datasets. The proposed model reached a mean Average Precision (mAP) of 0.96 and processing times of less than 1s. The results demonstrate the feasibility of deep convolutional neural networks for real-time particle size segmentation and establishes the first step towards a novel Edge AI system for particle size measurement in mineral processing plants. (More)

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Paper citation in several formats:
Cardoso, F.; Silva, M.; Meira, N.; Oliveira, R. and Bianchi, A. (2023). Towards a Novel Edge AI System for Particle Size Detection in Mineral Processing Plants. In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-648-4; ISSN 2184-4992, SciTePress, pages 312-323. DOI: 10.5220/0011748000003467

@conference{iceis23,
author={Flávio Cardoso. and Mateus Silva. and Natália Meira. and Ricardo Oliveira. and Andrea Bianchi.},
title={Towards a Novel Edge AI System for Particle Size Detection in Mineral Processing Plants},
booktitle={Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2023},
pages={312-323},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011748000003467},
isbn={978-989-758-648-4},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Towards a Novel Edge AI System for Particle Size Detection in Mineral Processing Plants
SN - 978-989-758-648-4
IS - 2184-4992
AU - Cardoso, F.
AU - Silva, M.
AU - Meira, N.
AU - Oliveira, R.
AU - Bianchi, A.
PY - 2023
SP - 312
EP - 323
DO - 10.5220/0011748000003467
PB - SciTePress

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