Výsledky vyhledávání - "Pickel, Tobias"

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Georeferencing of Photovoltaic Modules from Aerial Infrared Videos using Structure-from-Motion

Autor: Bommes, Lukas, Buerhop-Lutz, Claudia, Pickel, Tobias, Hauch, Jens, Brabec, Christoph, Peters, Ian Marius

To identify abnormal photovoltaic (PV) modules in large-scale PV plants economically, drone-mounted infrared (IR) cameras and automated video processing algorithms are frequently used. While most related works focus on the detection of abnormal modul

Externí odkaz: http://arxiv.org/abs/2204.02733

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Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning

Autor: Bommes, Lukas, Hoffmann, Mathis, Buerhop-Lutz, Claudia, Pickel, Tobias, Hauch, Jens, Brabec, Christoph, Maier, Andreas, Peters, Ian Marius

Increasing deployment of photovoltaic (PV) plants requires methods for automatic detection of faulty PV modules in modalities, such as infrared (IR) images. Recently, deep learning has become popular for this. However, related works typically sample

Externí odkaz: http://arxiv.org/abs/2112.02922

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Computer Vision Tool for Detection, Mapping and Fault Classification of PV Modules in Aerial IR Videos

Autor: Bommes, Lukas, Pickel, Tobias, Buerhop-Lutz, Claudia, Hauch, Jens, Brabec, Christoph, Peters, Ian Marius

Increasing deployment of photovoltaics (PV) plants demands for cheap and fast inspection. A viable tool for this task is thermographic imaging by unmanned aerial vehicles (UAV). In this work, we develop a computer vision tool for the semi-automatic e

Externí odkaz: http://arxiv.org/abs/2106.07314

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PV Modules and Their Backsheets -- A Case Study of a Multi-MW PV Power Station

Autor: Buerhop-Lutz, Claudia, Stoyuk, Oleksandr, Pickel, Tobias, Winkler, Thilo, Hauch, Jens, Peters, Ian Marius

Degradation of backsheets (BS) and encapsulant polymer components of silicon PV modules is recognized as one of the main reasons for losses in PV plant performance and lifetime expectations. Here, we report first insights into the correlation between

Externí odkaz: http://arxiv.org/abs/2105.05462

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Deep Learning-based Pipeline for Module Power Prediction from EL Measurements

Autor: Hoffmann, Mathis, Buerhop-Lutz, Claudia, Reeb, Luca, Pickel, Tobias, Winkler, Thilo, Doll, Bernd, Würfl, Tobias, Peters, Ian Marius, Brabec, Christoph, Maier, Andreas, Christlein, Vincent

Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules but have not been used to determine the power

Externí odkaz: http://arxiv.org/abs/2009.14712

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