elpv
Classification
Industry
|...
许可协议: 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

数据概要
数据格式
Image,
数据量
2.624K
文件大小
89.02MB
发布方
Sergiu Deitsch
Researcher in the Computer Vision (CV) group at the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg
数据集反馈
| 106 | 数据量 2.624K | 大小 89.02MB
elpv
Classification
Industry
许可协议: 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

数据集反馈
0
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