A scalable framework for annotating photovoltaic cell defects in electroluminescence images
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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
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