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https://github.com/zae-bayern/elpv-dataset
A dataset of functional and defective solar cells extracted from EL images of solar modules
https://github.com/zae-bayern/elpv-dataset
computer-vision machine-learning photovoltaic solar-cells solar-energy
Last synced: about 2 months ago
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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 (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-10-13T20:58:12.000Z (3 months ago)
- Last Synced: 2024-10-29T22:30:42.556Z (2 months ago)
- Topics: computer-vision, machine-learning, photovoltaic, solar-cells, solar-energy
- Language: Python
- Homepage:
- Size: 88.7 MB
- Stars: 223
- Watchers: 14
- Forks: 75
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- open-sustainable-technology - elpv-dataset - A dataset of functional and defective solar cells extracted from EL images of solar modules. (Renewable Energy / Photovoltaics and Solar Energy)
README
# A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery
[](https://pypi.org/project/elpv-dataset)
[](https://pypi.org/project/elpv-dataset)
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
```console
pip install elpv-dataset
```
and load the images and the corresponding annotations as follows:
```python
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](http://dx.doi.org/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](https://doi.org/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](http://dx.doi.org/10.1016/j.solener.2019.02.067)
BibTeX details:
```bibtex
@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](https://www.apache.org/licenses/LICENSE-2.0.html).
For commercial use, please contact us for further information.