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elpv-dataset

A dataset of functional and defective solar cells extracted from EL images of solar modules

https://github.com/zae-bayern/elpv-dataset

Science Score: 57.0%

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    CITATION.cff file
    Found CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 14 DOI reference(s) in README
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    Scientific vocabulary similarity
    Low similarity (9.7%) to scientific vocabulary

Keywords

computer-vision machine-learning photovoltaic solar-cells solar-energy
Last synced: 6 months ago · JSON representation ·

Repository

A dataset of functional and defective solar cells extracted from EL images of solar modules

Basic Info
  • Host: GitHub
  • Owner: zae-bayern
  • License: other
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 88.7 MB
Statistics
  • Stars: 267
  • Watchers: 14
  • Forks: 81
  • Open Issues: 0
  • Releases: 2
Topics
computer-vision machine-learning photovoltaic solar-cells solar-energy
Created almost 8 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery

PyPI - Version PyPI - Python Version

This repository provides a dataset of solar cell images extracted from high-resolution electroluminescence images of photovoltaic modules.

An overview of images in the dataset. The darker the red is, the higher is the likelihood of a defect in the solar cell overlayed by the corresponding color.

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

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:

```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

Creative Commons 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.

Owner

  • Name: ZAE Bayern
  • Login: zae-bayern
  • Kind: organization

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

GitHub Events

Total
  • Watch event: 49
  • Delete event: 1
  • Issue comment event: 1
  • Push event: 3
  • Pull request event: 1
  • Fork event: 6
  • Create event: 2
Last Year
  • Watch event: 49
  • Delete event: 1
  • Issue comment event: 1
  • Push event: 3
  • Pull request event: 1
  • Fork event: 6
  • Create event: 2

Committers

Last synced: 7 months ago

All Time
  • Total Commits: 26
  • Total Committers: 1
  • Avg Commits per committer: 26.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 7
  • Committers: 1
  • Avg Commits per committer: 7.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Sergiu Deitsch s****h@g****m 26

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • 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 requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • zsxgb (1)
  • aizensousuke0413 (1)
  • TrulyPV (1)
  • cainsmile (1)
  • admin-zae (1)
Pull Request Authors
  • dependabot[bot] (1)
Top Labels
Issue Labels
question (1) invalid (1)
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 98 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

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 98 Last month
Rankings
Dependent packages count: 10.2%
Average: 33.8%
Dependent repos count: 57.4%
Maintainers (1)
sergiud
Last synced: 6 months ago

Dependencies

.github/workflows/linux.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
pyproject.toml pypi
  • numpy *
  • pillow *