2022
Fioresi, Joseph; Colvin, Dylan J.; Frota, Rafaela; Gupta, Rohit; Li, Mengjie; Seigneur, Hubert P.; Vyas, Shruti; Oliveira, Sofia; Shah, Mubarak; Davis, Kristopher O.
Automated Defect Detection and Localization in Photovoltaic Cells Using Semantic Segmentation of Electroluminescence Images Journal Article
In: IEEE Journal of Photovoltaics, vol. 12, no. 1, pp. 53-61, 2022.
Abstract | Tags: REU, Semantic Segmentation, Solar Cells | Links:
@article{Fioresi2022,
title = {Automated Defect Detection and Localization in Photovoltaic Cells Using Semantic Segmentation of Electroluminescence Images},
author = {Joseph Fioresi and Dylan J. Colvin and Rafaela Frota and Rohit Gupta and Mengjie Li and Hubert P. Seigneur and Shruti Vyas and Sofia Oliveira and Mubarak Shah and Kristopher O. Davis},
url = {https://ieeexplore.ieee.org/document/9650542},
doi = {10.1109/JPHOTOV.2021.3131059},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Journal of Photovoltaics},
volume = {12},
number = {1},
pages = {53-61},
abstract = {In this article, we propose a deep learning based semantic segmentation model that identifies and segments defects in electroluminescence (EL) images of silicon photovoltaic (PV) cells. The proposed model can differentiate between cracks, contact interruptions, cell interconnect failures, and contact corrosion for both multicrystalline and monocrystalline silicon cells. Our model utilizes a segmentation Deeplabv3 model with a ResNet-50 backbone. It was trained on 17,064 EL images including 256 physically realistic simulated images of PV cells generated to deal with class imbalance. While performing semantic segmentation for five defect classes, this model achieves a weighted F1-score of 0.95, an unweighted F1-score of 0.69, a pixel-level global accuracy of 95.4%, and a mean intersection over union score of 57.3%. In addition, we introduce the UCF EL Defect dataset, a large-scale dataset consisting of 17,064 EL images, which will be publicly available for use by the PV and computer vision research communities.},
keywords = {REU, Semantic Segmentation, Solar Cells},
pubstate = {published},
tppubtype = {article}
}
In this article, we propose a deep learning based semantic segmentation model that identifies and segments defects in electroluminescence (EL) images of silicon photovoltaic (PV) cells. The proposed model can differentiate between cracks, contact interruptions, cell interconnect failures, and contact corrosion for both multicrystalline and monocrystalline silicon cells. Our model utilizes a segmentation Deeplabv3 model with a ResNet-50 backbone. It was trained on 17,064 EL images including 256 physically realistic simulated images of PV cells generated to deal with class imbalance. While performing semantic segmentation for five defect classes, this model achieves a weighted F1-score of 0.95, an unweighted F1-score of 0.69, a pixel-level global accuracy of 95.4%, and a mean intersection over union score of 57.3%. In addition, we introduce the UCF EL Defect dataset, a large-scale dataset consisting of 17,064 EL images, which will be publicly available for use by the PV and computer vision research communities.
Fioresi, Joseph; Colvin, Dylan J.; Frota, Rafaela; Gupta, Rohit; Li, Mengjie; Seigneur, Hubert P.; Vyas, Shruti; Oliveira, Sofia; Shah, Mubarak; Davis, Kristopher O.
Automated Defect Detection and Localization in Photovoltaic Cells Using Semantic Segmentation of Electroluminescence Images Journal Article
In: IEEE Journal of Photovoltaics, vol. 12, no. 1, pp. 53-61, 2022.
Abstract | Tags: REU, Semantic Segmentation, Solar Cells | Links:
@article{Fioresi2022b,
title = {Automated Defect Detection and Localization in Photovoltaic Cells Using Semantic Segmentation of Electroluminescence Images},
author = {Joseph Fioresi and Dylan J. Colvin and Rafaela Frota and Rohit Gupta and Mengjie Li and Hubert P. Seigneur and Shruti Vyas and Sofia Oliveira and Mubarak Shah and Kristopher O. Davis},
url = {https://ieeexplore.ieee.org/document/9650542},
doi = {10.1109/JPHOTOV.2021.3131059},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Journal of Photovoltaics},
volume = {12},
number = {1},
pages = {53-61},
abstract = {In this article, we propose a deep learning based semantic segmentation model that identifies and segments defects in electroluminescence (EL) images of silicon photovoltaic (PV) cells. The proposed model can differentiate between cracks, contact interruptions, cell interconnect failures, and contact corrosion for both multicrystalline and monocrystalline silicon cells. Our model utilizes a segmentation Deeplabv3 model with a ResNet-50 backbone. It was trained on 17,064 EL images including 256 physically realistic simulated images of PV cells generated to deal with class imbalance. While performing semantic segmentation for five defect classes, this model achieves a weighted F1-score of 0.95, an unweighted F1-score of 0.69, a pixel-level global accuracy of 95.4%, and a mean intersection over union score of 57.3%. In addition, we introduce the UCF EL Defect dataset, a large-scale dataset consisting of 17,064 EL images, which will be publicly available for use by the PV and computer vision research communities.},
keywords = {REU, Semantic Segmentation, Solar Cells},
pubstate = {published},
tppubtype = {article}
}
In this article, we propose a deep learning based semantic segmentation model that identifies and segments defects in electroluminescence (EL) images of silicon photovoltaic (PV) cells. The proposed model can differentiate between cracks, contact interruptions, cell interconnect failures, and contact corrosion for both multicrystalline and monocrystalline silicon cells. Our model utilizes a segmentation Deeplabv3 model with a ResNet-50 backbone. It was trained on 17,064 EL images including 256 physically realistic simulated images of PV cells generated to deal with class imbalance. While performing semantic segmentation for five defect classes, this model achieves a weighted F1-score of 0.95, an unweighted F1-score of 0.69, a pixel-level global accuracy of 95.4%, and a mean intersection over union score of 57.3%. In addition, we introduce the UCF EL Defect dataset, a large-scale dataset consisting of 17,064 EL images, which will be publicly available for use by the PV and computer vision research communities.