Abstract:
An important technique for the quality assurance of photovoltaic (PV) panels during the manufacturing process is the analysis of electroluminescence (EL) images. Various ...Show MoreMetadata
Abstract:
An important technique for the quality assurance of photovoltaic (PV) panels during the manufacturing process is the analysis of electroluminescence (EL) images. Various defects can be detected such as scratches, cracks, and short circuit cells. In our paper we introduce a new EL image dataset of monocrystalline panels and compare different loss functions for the detection of defects with state-of-the-art YOLOv5 deep neural network. For the segmentation of cells and to be able to make a geometrical description of panels we use an image-gradient based optimization approach. These information lead to a detailed structural and qualitative characterization of PV modules.
Published in: 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)
Date of Conference: 06-09 September 2022
Date Added to IEEE Xplore: 25 October 2022
ISBN Information: