A Gradient Guided Architecture Coupled With Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces

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...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.58950-58964
Main Authors: Hussain, Muhammad, Chen, Tianhua, Titrenko, Sofya, Su, Pan, Mahmud, Mufti
Format: Article
Language:English
Subjects:
Architectural complexities
Architecture
Artificial neural networks
Computational modeling
Computer architecture
convolutional neural network
Convolutional neural networks
Data integration
Deep learning
electroluminescence modelling
filter fused augmentations
filter tuning
Generative adversarial networks
Image filters
Image quality
Manufacturing
micro-crack
Microcracks
Photovoltaic cells
photovoltaics
Production
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3178588