A scalable framework for annotating photovoltaic cell defects in electroluminescence images

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 9; pp. 1 - 8
• Main Authors: Otamendi, Urtzi, Martinez, Inigo, Olaizola, Igor G., Quartulli, Marco
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Anomalies; anomaly segmentation; benchmark dataset; Costs; Datasets; Decision making; deep learning; defect annotation; Electroluminescence; Golden Standard; Image segmentation; Photovoltaic cells; Pipelines; Training
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3228680

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%.