  The correct functioning of photovoltaic (PV) cells is critical to ensuring
the optimal performance of a solar plant. Anomaly detection techniques for PV
cells can result in significant cost savings in operation and maintenance
(O&M). Recent research has focused on deep learning techniques for
automatically detecting anomalies in Electroluminescence (EL) images. Automated
anomaly annotations can improve current O&M methodologies and help develop
decision-making systems to extend the life-cycle of the PV cells and predict
failures. This paper addresses the lack of anomaly segmentation annotations in
the literature by proposing a combination of state-of-the-art data-driven
techniques to create a Golden Standard benchmark. The proposed method stands
out for (1) its adaptability to new PV cell types, (2) cost-efficient
fine-tuning, and (3) leverage public datasets to generate advanced annotations.
The methodology has been validated in the annotation of a widely used dataset,
obtaining a reduction of the annotation cost by 60%.
