TY - JOUR
T1 - A Gradient Guided Architecture Coupled With Filter Fused Representations for Micro-Crack Detection in Photovoltaic Cell Surfaces
AU - Hussain, Muhammad
AU - Chen, Tianhua
AU - Titarenko, Sofya
AU - Su, Pan
AU - Mahmud, Mufti
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022/6/8
Y1 - 2022/6/8
N2 - 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.
AB - 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.
KW - Architectural complexities
KW - Electroluminescence modelling
KW - Filter fused augmentations
KW - Filter tuning
KW - Micro-crack
KW - Photovoltaics
KW - Convolutional neural network
KW - Deep learning
UR - http://www.scopus.com/inward/record.url?scp=85132075086&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3178588
DO - 10.1109/ACCESS.2022.3178588
M3 - Article
VL - 10
SP - 58950
EP - 58964
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -