Hussain, M, Chen, T, Titrenko, S et al. (2 more authors) (2022) A Gradient Guided Architecture Coupled With Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces. IEEE Access, 10. pp. 58950-58964. ISSN 2169-3536
Abstract
This paper presents a shallow architecture based on Convolutional Neural Networks (CNN) for detecting Micro-cracks in Photovoltaic (PV) cells within the manufacturing environment. Based on Electro Luminescence (EL) imaging principles, this research presents a mechanism for determining the number of filters within the convolutional blocks, gradient guided filter tuning (GGFT). Observing the similarity between the original EL images and the filter output images obtained via GGFT, the research further introduces a mechanism for generating PV cell images based on EL Modelling, termed Filter Fused Data Scaling (FFDS). The effectiveness of both techniques is presented by benchmarking our developed architecture against ‘off the shelf’ augmentations and State-of-the-Art (SOTA) networks. The performance criteria was widened to include accuracy, computational, architectural, and post-deployment metrics. The high performance of our architecture in an intensive and wide-scoped evaluation demonstrates the high efficacy of our proposed mechanisms for developing PV-specific architectures and addressing the issue of data scarcity, particularly the difficulty in the procurement of quality EL images from the manufacturing site.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Keywords: | Architectural complexities, electroluminescence modelling, filter fused augmentations, filter tuning, micro-crack, photovoltaics, convolutional neural network, deep learning. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Jul 2022 16:30 |
Last Modified: | 05 Jul 2022 16:30 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/access.2022.3178588 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:188491 |