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

2 code implementations • 6 Apr 2022 • Lukas Bommes, Claudia Buerhop-Lutz, Tobias Pickel, Jens Hauch, Christoph Brabec, Ian Marius Peters

Comparison with an accurate orthophoto of one of the large-scale plants yields a root mean square error of the estimated module geocoordinates of 5. 87 m and a relative error within each plant row of 0. 22 m to 0. 82 m. Finally, we use the module geocoordinates and extracted IR images to visualize distributions of module temperatures and anomaly predictions of a deep learning classifier on a map.

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

2 code implementations • 6 Dec 2021 • Lukas Bommes, Mathis Hoffmann, Claudia Buerhop-Lutz, Tobias Pickel, Jens Hauch, Christoph Brabec, Andreas Maier, Ian Marius Peters

Instead, we frame fault detection as more realistic unsupervised domain adaptation problem where we train on labelled data of one source PV plant and make predictions on another target plant.

Anomaly Detection Contrastive Learning +2

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

2 code implementations • 14 Jun 2021 • Lukas Bommes, Tobias Pickel, Claudia Buerhop-Lutz, Jens Hauch, Christoph Brabec, Ian Marius Peters

In this work, we develop a computer vision tool for the semi-automatic extraction of PV modules from thermographic UAV videos.

Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multi-Component Systems

1 code implementation • 8 Sep 2019 • Stefan Langner, Florian Häse, José Darío Perea, Tobias Stubhan, Jens Hauch, Loïc M. Roch, Thomas Heumueller, Alán Aspuru-Guzik, Christoph J. Brabec

Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends which represents a clear trend towards multi-component active layer blends.

Applied Physics