10.26165/JUELICH-DATA/GCBNMABuerhop-Lutz, ClaudiaClaudiaBuerhop-LutzZAE BayernDeitsch, SergiuSergiuDeitschTechnische Hochschule Nürnberg Georg Simon OhmMaier, AndreasAndreasMaierFriedrich-Alexander-Universität Erlangen-NürnbergGallwitz, FlorianFlorianGallwitzTechnische Hochschule Nürnberg Georg Simon OhmBerger, StephanStephanBergerZAE BayernDoll, BerndBerndDollZAE BayernHauch, JensJensHauchZAE BayernCamus, ChristianChristianCamusZAE BayernBrabec, Christoph J.Christoph J.BrabecZAE BayernJülich DATA2020Computer and Information ScienceEarth and Environmental SciencesEngineeringEL-imaging, visual inspection, machine learningBuerhop-Lutz, ClaudiaClaudiaBuerhop-LutzForschungszentrum Jülich2020-08-062022-03-16solar cell electroluminescence images10.4229/35thEUPVSEC20182018-5CV.3.151806.0653010.1016/j.solener.2019.02.06793348939application/zip1.5This 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--