<?xml version='1.0' encoding='UTF-8'?><codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>EL Dataset of PV modules</titl><IDNo agency="DOI">doi:10.26165/JUELICH-DATA/GCBNMA</IDNo></titlStmt><distStmt><distrbtr source="archive">Jülich DATA</distrbtr><distDate>2020-08-06</distDate></distStmt><verStmt source="DVN"><version date="2022-03-16" type="RELEASED">1</version></verStmt><biblCit>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</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>EL Dataset of PV modules</titl><IDNo agency="DOI">doi:10.26165/JUELICH-DATA/GCBNMA</IDNo></titlStmt><rspStmt><AuthEnty affiliation="ZAE Bayern">Buerhop-Lutz, Claudia</AuthEnty><AuthEnty affiliation="Technische Hochschule Nürnberg Georg Simon Ohm">Deitsch, Sergiu</AuthEnty><AuthEnty affiliation="Friedrich-Alexander-Universität Erlangen-Nürnberg">Maier, Andreas</AuthEnty><AuthEnty affiliation="Technische Hochschule Nürnberg Georg Simon Ohm">Gallwitz, Florian</AuthEnty><AuthEnty affiliation="ZAE Bayern">Berger, Stephan</AuthEnty><AuthEnty affiliation="ZAE Bayern">Doll, Bernd</AuthEnty><AuthEnty affiliation="ZAE Bayern">Hauch, Jens</AuthEnty><AuthEnty affiliation="ZAE Bayern">Camus, Christian</AuthEnty><AuthEnty affiliation="ZAE Bayern">Brabec, Christoph J.</AuthEnty></rspStmt><prodStmt/><distStmt><distrbtr source="archive">Jülich DATA</distrbtr><contact affiliation="Forschungszentrum Jülich" email="c.buerhop-lutz@fz-juelich.de">Buerhop-Lutz, Claudia</contact><depositr>Denz, Janine</depositr><depDate>2020-08-06</depDate></distStmt></citation><stdyInfo><subject><keyword>Computer and Information Science</keyword><keyword>Earth and Environmental Sciences</keyword><keyword>Engineering</keyword><keyword>EL-imaging, visual inspection, machine learning</keyword></subject><abstract date="2018">This repository provides a dataset of solar cell images extracted from high-resolution electroluminescence images of photovoltaic modules.&#xd;
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.&#xd;
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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.&#xd;
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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.&#xd;
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The individual images are stored in the images directory and the corresponding annotations in labels.csv.&#xd;
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--More explanations in the README file--</abstract><sumDscr><dataKind>solar cell electroluminescence  images</dataKind></sumDscr></stdyInfo><method><dataColl><sources/></dataColl><anlyInfo/></method><dataAccs><setAvail/><useStmt/></dataAccs><othrStdyMat><relPubl><citation><titlStmt><IDNo agency="doi">10.4229/35thEUPVSEC20182018-5CV.3.15</IDNo></titlStmt><biblCit>Buerhop-Lutz, C.; Deitsch, S.; Maier, A.; Gallwitz, F.; Berger, S.; Doll, B.; Hauch, J.; Camus, C. &amp; 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.</biblCit></citation></relPubl><relPubl><citation><titlStmt><IDNo agency="arXiv">1806.06530</IDNo></titlStmt><biblCit>Deitsch, S.; Buerhop-Lutz, C.; Maier, A. K.; Gallwitz, F. &amp; Riess, C. Segmentation of Photovoltaic Module Cells in Electroluminescence Images. CoRR, 2018, abs/1806.06530</biblCit></citation></relPubl><relPubl><citation><titlStmt><IDNo agency="doi">10.1016/j.solener.2019.02.067</IDNo></titlStmt><biblCit>Deitsch, S.; Christlein, V.; Berger, S.; Buerhop-Lutz, C.; Maier, A.; Gallwitz, F. &amp; Riess, C. Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy, Elsevier BV, 2019, 185, 455-468.</biblCit></citation></relPubl></othrStdyMat></stdyDscr><otherMat ID="f1061" URI="https://doi.org/10.26165/JUELICH-DATA/GCBNMA/89GWQD" level="datafile"><labl>elpv-dataset-master.zip</labl><txt>Zip-file of repository</txt><notes level="file" type="DATAVERSE:CONTENTTYPE" subject="Content/MIME Type">application/zip</notes></otherMat></codeBook>