A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision

The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global warming. The manufacturing of solar cells can be defined as a rigorous process starting with silicon extraction. The increase in demand has multiple implications for manual quality inspection. With automated inspection as the ultimate goal, researchers are actively experimenting with convolutional neural network architectures. This review presents an overview of the electroluminescence image-extraction process, conventional image-processing techniques deployed for solar cell defect detection, arising challenges, the present landscape shifting towards computer vision architectures, and emerging trends.

Automated summary

The past two decades have witnessed an increase in the deployment of photovoltaic installations worldwide as an effort to mitigate the consequences of global warming. The manufacturing of solar cells involves a rigorous process starting from silicon extraction. The growing demand poses various challenges for manual quality inspection, leading researchers to explore convolutional neural network architectures for automated inspection.