Awesome

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

<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />All the images in this work are licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>. Accompanying Python source code is distributed under the terms of the Apache License 2.0.

For commercial use, please contact us for further information.