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Part 1: Document Description
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Citation |
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Title: |
EL Dataset of PV modules |
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Identification Number: |
doi:10.26165/JUELICH-DATA/GCBNMA |
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Distributor: |
Jülich DATA |
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Date of Distribution: |
2020-08-06 |
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Version: |
1 |
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Bibliographic Citation: |
Buerhop-Lutz, Claudia; Deitsch, Sergiu; Maier, Andreas; Gallwitz, Florian; Berger, Stephan; Doll, Bernd; Hauch, Jens; Camus, Christian; Brabec, Christoph J., 2020, "EL Dataset of PV modules", https://doi.org/10.26165/JUELICH-DATA/GCBNMA, Jülich DATA, V1 |
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Citation |
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Title: |
EL Dataset of PV modules |
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Identification Number: |
doi:10.26165/JUELICH-DATA/GCBNMA |
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Authoring Entity: |
Buerhop-Lutz, Claudia (ZAE Bayern) |
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Deitsch, Sergiu (Technische Hochschule Nürnberg Georg Simon Ohm) |
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Maier, Andreas (Friedrich-Alexander-Universität Erlangen-Nürnberg) |
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Gallwitz, Florian (Technische Hochschule Nürnberg Georg Simon Ohm) |
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Berger, Stephan (ZAE Bayern) |
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Doll, Bernd (ZAE Bayern) |
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Hauch, Jens (ZAE Bayern) |
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Camus, Christian (ZAE Bayern) |
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Brabec, Christoph J. (ZAE Bayern) |
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Distributor: |
Jülich DATA |
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Access Authority: |
Buerhop-Lutz, Claudia |
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Depositor: |
Denz, Janine |
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Date of Deposit: |
2020-08-06 |
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Study Scope |
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Keywords: |
Computer and Information Science, Earth and Environmental Sciences, Engineering, EL-imaging, visual inspection, machine learning |
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Abstract: |
This repository provides a dataset of solar cell images extracted from high-resolution electroluminescence images of photovoltaic modules. 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. 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. The individual images are stored in the images directory and the corresponding annotations in labels.csv. --More explanations in the README file-- |
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Kind of Data: |
solar cell electroluminescence images |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.4229/35thEUPVSEC20182018-5CV.3.15 |
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Bibliographic Citation: |
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. |
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Citation |
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Identification Number: |
1806.06530 |
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Bibliographic Citation: |
Deitsch, S.; Buerhop-Lutz, C.; Maier, A. K.; Gallwitz, F. & Riess, C. Segmentation of Photovoltaic Module Cells in Electroluminescence Images. CoRR, 2018, abs/1806.06530 |
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Citation |
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Identification Number: |
10.1016/j.solener.2019.02.067 |
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Bibliographic Citation: |
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. |
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Label: |
elpv-dataset-master.zip |
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Text: |
Zip-file of repository |
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Notes: |
application/zip |