Search Results for author:
Found 7 papers, 2 papers with code
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning
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.
Module-Power Prediction from PL Measurements using Deep Learning
While these areas can be easily identified from electroluminescense (EL) images, this is much harder for photoluminescence (PL) images.
Joint Super-Resolution and Rectification for Solar Cell Inspection
Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants.
Deep Learning-based Pipeline for Module Power Prediction from EL Measurements
However, knowledge of the power at maximum power point is important as well, since drops in the power of a single module can affect the performance of an entire string.
Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm
To this end, we apply normalized Lp normalization to aggregate the activation maps into single scores for classification.
Fast and robust detection of solar modules in electroluminescence images
We compare our method to the state of the art and show that it is superior in presence of perspective distortion while the performance on images, where the module is roughly coplanar to the detector, is similar to the reference method.
Learning with Known Operators reduces Maximum Training Error Bounds
We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.
