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elpv
Classification
Industry
|...
License: CC BY-NC-SA 4.0

Overview

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.

Data Format

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.

Instruction

In Python, use utils/elpv_reader in this repository to load the images and the corresponding
annotations as follows:

from elpv_reader import load_dataset
images, proba, types = load_dataset()

The code requires NumPy and Pillow to work correctly.

Citation

Please use the following citation when referencing the dataset:

@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},
}

@TechReport{Deitsch2018,
  Title            = {Segmentation of Photovoltaic Module Cells in Electroluminescence Images},
  Author           = {Sergiu Deitsch and Claudia Buerhop-Lutz and Andreas K. Maier
and Florian Gallwitz and Christian Riess},
  Year             = {2018},
  Archiveprefix    = {arXiv},
  Eprint           = {1806.06530},
  Journal          = {CoRR},
  Url              = {http://arxiv.org/abs/1806.06530},
  Volume           = {abs/1806.06530}
}


@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

CC BY-NC-SA 4.0

Data Summary
Type
Image,
Amount
2.624K
Size
89.02MB
Provided by
Sergiu Deitsch
Researcher in the Computer Vision (CV) group at the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg
| Amount 2.624K | Size 89.02MB
elpv
Classification
Industry
License: CC BY-NC-SA 4.0

Overview

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.

Data Format

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.

Instruction

In Python, use utils/elpv_reader in this repository to load the images and the corresponding
annotations as follows:

from elpv_reader import load_dataset
images, proba, types = load_dataset()

The code requires NumPy and Pillow to work correctly.

Citation

Please use the following citation when referencing the dataset:

@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},
}

@TechReport{Deitsch2018,
  Title            = {Segmentation of Photovoltaic Module Cells in Electroluminescence Images},
  Author           = {Sergiu Deitsch and Claudia Buerhop-Lutz and Andreas K. Maier
and Florian Gallwitz and Christian Riess},
  Year             = {2018},
  Archiveprefix    = {arXiv},
  Eprint           = {1806.06530},
  Journal          = {CoRR},
  Url              = {http://arxiv.org/abs/1806.06530},
  Volume           = {abs/1806.06530}
}


@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

CC BY-NC-SA 4.0

0
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