Electroluminescence Image

Electroluminescence (EL) imaging provides a non-destructive method for detecting defects in photovoltaic (PV) solar cells, crucial for efficient solar energy harvesting. Current research heavily focuses on automating defect detection in EL images using deep learning models, including convolutional neural networks (CNNs) and semantic segmentation approaches, often employing techniques like semi-supervised learning and transfer learning to address data limitations and improve efficiency. These advancements aim to reduce the cost and time associated with manual inspection, leading to improved quality control in solar cell manufacturing and increased reliability of solar energy systems. Lightweight model architectures are also being developed for deployment in resource-constrained industrial settings.