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A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision

Author

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  • Tahir Hussain

    (Department of Computer Science, Centre for Industrial Analytics, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Muhammad Hussain

    (Department of Computer Science, Centre for Industrial Analytics, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Hussain Al-Aqrabi

    (Department of Computer Information System (CIS), Higher Colleges of Technology, Sharjah P.O. Box 7947, United Arab Emirates)

  • Tariq Alsboui

    (Department of Computer Science, Centre for Industrial Analytics, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Richard Hill

    (Department of Computer Science, Centre for Industrial Analytics, School of Computing and Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global warming. The manufacturing of solar cells can be defined as a rigorous process starting with silicon extraction. The increase in demand has multiple implications for manual quality inspection. With automated inspection as the ultimate goal, researchers are actively experimenting with convolutional neural network architectures. This review presents an overview of the electroluminescence image-extraction process, conventional image-processing techniques deployed for solar cell defect detection, arising challenges, the present landscape shifting towards computer vision architectures, and emerging trends.

Suggested Citation

  • Tahir Hussain & Muhammad Hussain & Hussain Al-Aqrabi & Tariq Alsboui & Richard Hill, 2023. "A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision," Energies, MDPI, vol. 16(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4012-:d:1143697
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    References listed on IDEAS

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    1. Muhammad Hussain & Hussain Al-Aqrabi & Richard Hill, 2022. "Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection," Energies, MDPI, vol. 15(15), pages 1-14, July.
    2. Muhammad Hussain & Hussain Al-Aqrabi & Richard Hill, 2022. "PV-CrackNet Architecture for Filter Induced Augmentation and Micro-Cracks Detection within a Photovoltaic Manufacturing Facility," Energies, MDPI, vol. 15(22), pages 1-16, November.
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