Deep learning for photovoltaic defect detection using variational autoencoders.

The article discusses the importance of detecting faults in photovoltaic (PV) systems in order to improve efficiency, reliability, and safety using thermal images and computer vision. Topics include the use of variational autoencoders (VAEs) to artificially expand the data set and improve the classification task, use of three convolutional neural network (CNN) models for the classification of the images, and effectivenessof CNN models in detecting and classifying PV faults from thermal images.