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Vision-based size classification of iron ore pellets using ensembled convolutional neural network

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Abstract

In an iron ore pelletization plant, pellets are produced inside a rotating disc pelletizer. Online pellet size distribution is an important performance indicator of the pelletization process. Image processing-based system is an effective solution for online size analysis of iron ore pellets. This paper proposes a machine learning algorithm for estimating the size class of the pellets during their production by imaging from an area inside the disc pelletizer. Instead of computing the size of each individual pellets in the acquired image, this method proposes a qualitative approach to get the overall size estimate of the pellets in production. The key idea of this paper is to find out whether the disc is producing VERY SMALL, SMALL, MEDIUM, or BIG-sized pellets. A weighted average ensemble of different convolutional neural networks such as VGG16, Mobilenet, and Resnet50 is used to achieve this objective. Furthermore, batch normalization is applied to improve the estimation performance of the proposed model. A novel data augmentation method is applied to the in situ captured images to create the data set used to train and evaluate the proposed ensemble of CNN models. Results of experiments indicate that it is possible to detect the operating state of the pelletization disc by acquiring images from the inside area of the disc with sufficient accuracy.

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Code availability

The image dataset used for training and evaluation of the proposed model and the source codes are available at https://bit.ly/3b4eTQ4.

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Acknowledgements

Arya Jyoti Deo acknowledges the Council of Scientific and Industrial Research (CSIR), India, for providing financial support under the Senior Research Fellowship scheme (File No. 31/9(0141)/2018-EMR-I). Animesh Sahoo would like to thank CSIR-IMMT for providing the required facilities and support during his internship. Santosh Kumar Behera and Debi Prasad Das acknowledge Ministry of Steel, Govt. of India for financial support under Sanction No. 44(i) dated 07/03/2022  and CSIR-IMMT-project GAP-363 for a part of this work.

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Authors and Affiliations

  1. Process Engineering and Instrumentation Department, CSIR-Institute of Minerals and Materials Technology, Bhubaneswar, 751013, India

    Arya Jyoti Deo, Santosh Kumar Behera & Debi Prasad Das

  2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India

    Arya Jyoti Deo, Santosh Kumar Behera & Debi Prasad Das

  3. Birla Institute of Technology and Science, K. K. Birla Goa Campus, Sancoale, Goa, 403726, India

    Animesh Sahoo

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  1. Arya Jyoti Deo
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Correspondence to Santosh Kumar Behera.

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Deo, A.J., Sahoo, A., Behera, S.K. et al. Vision-based size classification of iron ore pellets using ensembled convolutional neural network. Neural Comput & Applic 34, 18629–18641 (2022). https://doi.org/10.1007/s00521-022-07473-1

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