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Identifying defective solar cells in electroluminescence images using deep feature representations
Electroluminescence (EL) imaging is a technique for acquiring images of photovoltaic (PV) modules and examining them for surface defects. Analysis of EL images has been manually performed by visual inspection of images by experts. This manual procedure is tedious, time-consuming, subjective, and req...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138174/ https://www.ncbi.nlm.nih.gov/pubmed/35634101 http://dx.doi.org/10.7717/peerj-cs.992 |
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author | Al‐Waisy, Alaa S. Ibrahim, Dheyaa Ahmed Zebari, Dilovan Asaad Hammadi, Shumoos Mohammed, Hussam Mohammed, Mazin Abed Damaševičius, Robertas |
author_facet | Al‐Waisy, Alaa S. Ibrahim, Dheyaa Ahmed Zebari, Dilovan Asaad Hammadi, Shumoos Mohammed, Hussam Mohammed, Mazin Abed Damaševičius, Robertas |
author_sort | Al‐Waisy, Alaa S. |
collection | PubMed |
description | Electroluminescence (EL) imaging is a technique for acquiring images of photovoltaic (PV) modules and examining them for surface defects. Analysis of EL images has been manually performed by visual inspection of images by experts. This manual procedure is tedious, time-consuming, subjective, and requires deep expert knowledge. In this work, a hybrid and fully-automated classification system is developed for detecting different types of defects in EL images. The system fuses the deep feature representations extracted from two different deep learning models (Inception-V3 and ResNet50) to form more discriminative feature vectors. These feature vectors are then fed into the classifier layer to assign them into one of different types of defects. A large-scale, challenging solar cells dataset composed of 2,624 EL images was used to assess the performance of the proposed system in both the binary classification (functional vs defective) task and multi-class classification (functional, mild, moderate, and severe) task. The proposed system has managed to detect the correct defect type with less than 1 s per image with an accuracy rate of 98.15% and 95.35% in the binary classification and multi-classification task, respectively. |
format | Online Article Text |
id | pubmed-9138174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91381742022-05-28 Identifying defective solar cells in electroluminescence images using deep feature representations Al‐Waisy, Alaa S. Ibrahim, Dheyaa Ahmed Zebari, Dilovan Asaad Hammadi, Shumoos Mohammed, Hussam Mohammed, Mazin Abed Damaševičius, Robertas PeerJ Comput Sci Artificial Intelligence Electroluminescence (EL) imaging is a technique for acquiring images of photovoltaic (PV) modules and examining them for surface defects. Analysis of EL images has been manually performed by visual inspection of images by experts. This manual procedure is tedious, time-consuming, subjective, and requires deep expert knowledge. In this work, a hybrid and fully-automated classification system is developed for detecting different types of defects in EL images. The system fuses the deep feature representations extracted from two different deep learning models (Inception-V3 and ResNet50) to form more discriminative feature vectors. These feature vectors are then fed into the classifier layer to assign them into one of different types of defects. A large-scale, challenging solar cells dataset composed of 2,624 EL images was used to assess the performance of the proposed system in both the binary classification (functional vs defective) task and multi-class classification (functional, mild, moderate, and severe) task. The proposed system has managed to detect the correct defect type with less than 1 s per image with an accuracy rate of 98.15% and 95.35% in the binary classification and multi-classification task, respectively. PeerJ Inc. 2022-05-19 /pmc/articles/PMC9138174/ /pubmed/35634101 http://dx.doi.org/10.7717/peerj-cs.992 Text en © 2022 Al‐Waisy et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Al‐Waisy, Alaa S. Ibrahim, Dheyaa Ahmed Zebari, Dilovan Asaad Hammadi, Shumoos Mohammed, Hussam Mohammed, Mazin Abed Damaševičius, Robertas Identifying defective solar cells in electroluminescence images using deep feature representations |
title | Identifying defective solar cells in electroluminescence images using deep feature representations |
title_full | Identifying defective solar cells in electroluminescence images using deep feature representations |
title_fullStr | Identifying defective solar cells in electroluminescence images using deep feature representations |
title_full_unstemmed | Identifying defective solar cells in electroluminescence images using deep feature representations |
title_short | Identifying defective solar cells in electroluminescence images using deep feature representations |
title_sort | identifying defective solar cells in electroluminescence images using deep feature representations |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138174/ https://www.ncbi.nlm.nih.gov/pubmed/35634101 http://dx.doi.org/10.7717/peerj-cs.992 |
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