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GANASUNet: An Efficient Convolutional Neural Architecture for Segmenting Iron Ore Images

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 717))

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Abstract

Iron ore segmentation has a challenge in segmenting different types of ores in the same area; the detection and segmentation of iron ore are used to analyze the material quality and optimize the plant processing. This paper presents an UNet-based Convolutional Neural Network (CNN) optimized by a technique so-called Neural Architecture Search(NAS) to segment fine iron ore regions. The images were collected from an iron ore plant, in which it was possible to obtain a dataset composed of 688 images and their label segmentation. The results of the optimized architecture show that the UNet-based architecture achieved a result of 80% of Intersect Over Union(IoU) against UNet without optimization with 75% and DeepLabV3+ with 78%, respectively.

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Author information

Authors and Affiliations

  1. Programa de Pós-Graduação em Engenharia da Computação e Sistemas (PECS), Universidade Estadual do Maranhão (UEMA), São Luis, MA, Brazil

    Ada Cristina França da Silva

  2. Departamento de Computação (DComp), Instituto Federal do Maranhão (IFMA), São Luis, MA, Brazil

    Omar Andres Carmona Cortes

Authors
  1. Ada Cristina França da Silva
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  2. Omar Andres Carmona Cortes
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Corresponding author

Correspondence to Omar Andres Carmona Cortes .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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da Silva, A.C.F., Cortes, O.A.C. (2023). GANASUNet: An Efficient Convolutional Neural Architecture for Segmenting Iron Ore Images. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_27

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