elpv-dataset
A dataset of functional and defective solar cells extracted from EL images of solar modules.
https://github.com/zae-bayern/elpv-dataset
Category: Renewable Energy
Sub Category: Photovoltaics and Solar Energy
Keywords
computer-vision machine-learning photovoltaic solar-cells solar-energy
Last synced: about 18 hours ago
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Repository metadata
A dataset of functional and defective solar cells extracted from EL images of solar modules
- Host: GitHub
- URL: https://github.com/zae-bayern/elpv-dataset
- Owner: zae-bayern
- License: other
- Created: 2018-03-07T10:53:56.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-10-13T20:58:12.000Z (7 months ago)
- Last Synced: 2025-04-10T05:05:06.038Z (17 days ago)
- Topics: computer-vision, machine-learning, photovoltaic, solar-cells, solar-energy
- Language: Python
- Homepage:
- Size: 88.7 MB
- Stars: 259
- Watchers: 14
- Forks: 78
- Open Issues: 0
- Releases: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
README.md
A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery
This repository provides a dataset of solar cell images extracted from
high-resolution electroluminescence images of photovoltaic modules.
The Dataset
The dataset contains 2,624 samples of 300x300 pixels 8-bit grayscale images of
functional and defective solar cells with varying degree of degradations
extracted from 44 different solar modules. The defects in the annotated images
are either of intrinsic or extrinsic type and are known to reduce the power
efficiency of solar modules.
All images are normalized with respect to size and perspective.
Additionally, any distortion induced by the camera lens used to capture the EL images was
eliminated prior to solar cell extraction.
Annotations
Every image is annotated with a defect probability (a floating point value
between 0 and 1) and the type of the solar module (either mono- or
polycrystalline) the solar cell image was originally extracted from.
Usage
Install the Python package
pip install elpv-dataset
and load the images and the corresponding annotations as follows:
from elpv_dataset.utils import load_dataset
images, proba, types = load_dataset()
Citing
If you use this dataset in scientific context, please cite the following
publications:
Buerhop-Lutz, C.; Deitsch, S.; Maier, A.; Gallwitz, F.; Berger, S.; Doll, B.; Hauch, J.; Camus, C. & Brabec, C. J. A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery. European PV Solar Energy Conference and Exhibition (EU PVSEC), 2018. DOI: 10.4229/35thEUPVSEC20182018-5CV.3.15
Deitsch, S., Buerhop-Lutz, C., Sovetkin, E., Steland, A., Maier, A., Gallwitz, F., & Riess, C. (2021). Segmentation of photovoltaic module cells in uncalibrated electroluminescence images. Machine Vision and Applications, 32(4). DOI: 10.1007/s00138-021-01191-9
Deitsch, S.; Christlein, V.; Berger, S.; Buerhop-Lutz, C.; Maier, A.; Gallwitz, F. & Riess, C. Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy, Elsevier BV, 2019, 185, 455-468. DOI: 10.1016/j.solener.2019.02.067
BibTeX details:
@InProceedings{Buerhop2018,
author = {Buerhop-Lutz, Claudia and Deitsch, Sergiu and Maier, Andreas and Gallwitz, Florian and Berger, Stephan and Doll, Bernd and Hauch, Jens and Camus, Christian and Brabec, Christoph J.},
title = {A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery},
booktitle = {European PV Solar Energy Conference and Exhibition (EU PVSEC)},
year = {2018},
eventdate = {2018-09-24/2018-09-28},
venue = {Brussels, Belgium},
doi = {10.4229/35thEUPVSEC20182018-5CV.3.15},
}
@Article{Deitsch2021,
author = {Deitsch, Sergiu and Buerhop-Lutz, Claudia and Sovetkin, Evgenii and Steland, Ansgar and Maier, Andreas and Gallwitz, Florian and Riess, Christian},
date = {2021},
journaltitle = {Machine Vision and Applications},
title = {Segmentation of photovoltaic module cells in uncalibrated electroluminescence images},
doi = {10.1007/s00138-021-01191-9},
issn = {1432-1769},
number = {4},
volume = {32},
}
@Article{Deitsch2019,
author = {Sergiu Deitsch and Vincent Christlein and Stephan Berger and Claudia Buerhop-Lutz and Andreas Maier and Florian Gallwitz and Christian Riess},
title = {Automatic classification of defective photovoltaic module cells in electroluminescence images},
journal = {Solar Energy},
year = {2019},
volume = {185},
pages = {455--468},
month = jun,
issn = {0038-092X},
doi = {10.1016/j.solener.2019.02.067},
publisher = {Elsevier {BV}},
}
License
All the images in this work are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Accompanying Python source code is distributed under the terms of the Apache License 2.0.
For commercial use, please contact us for further information.
Citation (CITATION.cff)
cff-version: 1.2.0 message: If you use this dataset in scientific context, please cite the publications below. title: A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery authors: - family-names: Buerhop-Lutz given-names: Claudia orcid: https://orcid.org/0000-0001-5233-6700 - family-names: Deitsch given-names: Sergiu orcid: https://orcid.org/0000-0001-8865-8066 - family-names: Maier given-names: Andreas orcid: https://orcid.org/0000-0002-9550-5284 - family-names: Gallwitz given-names: Florian orcid: https://orcid.org/0000-0002-1359-8633 - family-names: Berger given-names: Stephan - family-names: Doll given-names: Bernd - family-names: Hauch given-names: Jens - family-names: Camus given-names: Christian - family-names: Brabec given-names: Christoph date-released: 2018-03-07 preferred-citation: type: conference-paper authors: - family-names: Buerhop-Lutz given-names: Claudia orcid: https://orcid.org/0000-0001-5233-6700 - family-names: Deitsch given-names: Sergiu orcid: https://orcid.org/0000-0001-8865-8066 - family-names: Maier given-names: Andreas orcid: https://orcid.org/0000-0002-9550-5284 - family-names: Gallwitz given-names: Florian orcid: https://orcid.org/0000-0002-1359-8633 - family-names: Berger given-names: Stephan - family-names: Doll given-names: Bernd - family-names: Hauch given-names: Jens - family-names: Camus given-names: Christian - family-names: Brabec given-names: Christoph title: A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery conference: name: 35th European Photovoltaic Solar Energy Conference and Exhibition date-start: 2018-09-24 date-end: 2018-09-28 location: Brussels, Belgium start: 1287 end: 1289 month: 9 year: 2018 isbn: 3-936338-50-7 doi: 10.4229/35thEUPVSEC20182018-5CV.3.15 version: 1.0.0 license: CC-BY-NC-SA-4.0 repository-code: https://github.com/zae-bayern/elpv-dataset
Owner metadata
- Name: ZAE Bayern
- Login: zae-bayern
- Email:
- Kind: organization
- Description:
- Website: https://www.zae-bayern.de
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/37119743?v=4
- Repositories: 1
- Last ynced at: 2023-03-05T17:57:36.743Z
- Profile URL: https://github.com/zae-bayern
GitHub Events
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Last Year
- Watch event: 46
- Fork event: 6
Committers metadata
Last synced: 6 days ago
Total Commits: 26
Total Committers: 1
Avg Commits per committer: 26.0
Development Distribution Score (DDS): 0.0
Commits in past year: 7
Committers in past year: 1
Avg Commits per committer in past year: 7.0
Development Distribution Score (DDS) in past year: 0.0
Name | Commits | |
---|---|---|
Sergiu Deitsch | s****h@g****m | 26 |
Committer domains:
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 5
Total pull requests: 0
Average time to close issues: about 13 hours
Average time to close pull requests: N/A
Total issue authors: 5
Total pull request authors: 0
Average comments per issue: 1.4
Average comments per pull request: 0
Merged pull request: 0
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
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Past year pull request authors: 0
Past year average comments per issue: 0
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Past year merged pull request: 0
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Top Issue Authors
- zsxgb (1)
- aizensousuke0413 (1)
- TrulyPV (1)
- cainsmile (1)
- admin-zae (1)
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Top Issue Labels
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Package metadata
- Total packages: 1
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Total downloads:
- pypi: 431 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 3
- Total maintainers: 1
pypi.org: elpv-dataset
A dataset of functional and defective solar cells extracted from EL images of solar modules
- Homepage:
- Documentation: https://github.com/zae-bayern/elpv-dataset#readme
- Licenses: other
- Latest release: 1.0.0 (published 7 months ago)
- Last Synced: 2025-04-25T12:31:11.559Z (2 days ago)
- Versions: 3
- Dependent Packages: 0
- Dependent Repositories: 0
- Downloads: 431 Last month
-
Rankings:
- Dependent packages count: 10.205%
- Average: 33.819%
- Dependent repos count: 57.434%
- Maintainers (1)
Dependencies
- actions/checkout v4 composite
- actions/setup-python v5 composite
- numpy *
- pillow *
Score: 11.625253649943648