147 citations · 160 across the 8 of their papers we have counts for
8 papers
DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling
Dennis Possart, Leonid Mill, Florian Vollnhals +11
Nanomaterials exhibit distinctive properties governed by parameters such as size, shape, and surface characteristics, which critically influence their applications and interactions…
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning
Lukas Bommes, Mathis Hoffmann, Claudia Buerhop-Lutz +5
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…
Module-Power Prediction from PL Measurements using Deep Learning
Mathis Hoffmann, Johannes Hepp, Bernd Doll +5
The individual causes for power loss of photovoltaic modules are investigated for quite some time. Recently, it has been shown that the power loss of a module is, for example, rela…
Joint Super-Resolution and Rectification for Solar Cell Inspection
Mathis Hoffmann, Thomas Köhler, Bernd Doll +6
Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolutio…
Deep Learning-based Pipeline for Module Power Prediction from EL Measurements
Mathis Hoffmann, Claudia Buerhop-Lutz, Luca Reeb +8
Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of d…
Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm
Martin Mayr, Mathis Hoffmann, Andreas Maier +1
Photovoltaic is one of the most important renewable energy sources for dealing with world-wide steadily increasing energy consumption. This raises the demand for fast and scalable…
Fast and robust detection of solar modules in electroluminescence images
Mathis Hoffmann, Bernd Doll, Florian Talkenberg +3
Fast, non-destructive and on-site quality control tools, mainly high sensitive imaging techniques, are important to assess the reliability of photovoltaic plants. To minimize the r…
Learning with Known Operators reduces Maximum Training Error Bounds
Andreas K. Maier, Christopher Syben, Bernhard Stimpel +7
We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain op…