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Iron ore pellets measurement using deep learning based on YOLACT

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Abstract

The thermal efficiency for the pelletizing process is intrinsically linked to the diameter and humidity of the iron ore pellets, so that the sensing of the granulometric range in the formation of the pellets becomes essential to the flow of the pelletizing process in the steel industry; this paper presents the assembly of a computer vision system for the detection and segmentation of pellets aiming at the automation of the granulometric measurement, following its formation in the pelletizing disk, using the instance segmentation method to verify whether the particle granulometric distribution (PSD) is adequate for “real-time” applications. The system calculates the normal distribution of the diameter in millimeters, evaluating the normal curve and the standard deviation of the segmented pellets, using a deep neural network based on the You Only Look At CoefficienTs (YOLACT) network, adding speed and precision in the granulometric analysis. In the sample sets, the need for adjustment factors inherent to the pelletizing process became evident. This led to the establishment of the computer vision system, termed the Volumetric Correction Factor (VCF) and Visual Overlay Factor (VOF). The VCF is utilized to estimate the volume of pellets within the pelletizing disk during operation, while the VOF adjusts the millimeter-per-pixel (mpp) ratio. The results of the measurement system proved to be efficient in real-time granulometric measurement.

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Acknowledgments

The authors acknowledge the scientific and technical support of the Federal Institute of Espirito Santo and the cooperation of Vale.

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Authors and Affiliations

  1. Postgradude Program of Control and Automation Engineering, Federal Institute of Espírito Santo, Avenida dos Sabiás, Serra, Espirito Santo, 29166-630, Brazil

    Caio Mario Carletti Vilela Santos, Ricardo de Almeida, Carlos Torturella Valadao, Marco Antonio de Souza Leite Cuadros & Gustavo Maia de Almeida

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  1. Caio Mario Carletti Vilela Santos
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  2. Ricardo de Almeida
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  4. Marco Antonio de Souza Leite Cuadros
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  5. Gustavo Maia de Almeida
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Contributions

C.M.S., M.A.C., and G.A. were involved in conceptualization, methodology, software, and visualization. C.M.S., R.A., M.A.C., and G.A. were involved in validation, verification, formal analysis, investigation, and data curation. C.V., C.M.S., R.A., M.A.C., and G.A. helped with writing—original paper. C.V., C.M.V., M.A.C., and G.A. contributed to writing—review and editing. M.A.C. and G.A. were involved in project administration. M.A.C. and G.A.s helped with funding.

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Correspondence to Caio Mario Carletti Vilela Santos.

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Santos, C.M.C.V., de Almeida, R., Valadao, C.T. et al. Iron ore pellets measurement using deep learning based on YOLACT. Neural Comput & Applic (2024). https://doi.org/10.1007/s00521-024-09832-6

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