A scalable framework for annotating photovoltaic cell defects in electroluminescence images

TL;DR

This paper introduces a scalable, adaptable framework for annotating PV cell defects in electroluminescence images, significantly reducing annotation costs and enhancing anomaly detection for solar plant maintenance.

Contribution

It proposes a novel combination of data-driven techniques to generate a benchmark for PV defect annotation, improving adaptability and cost-efficiency over existing methods.

Findings

Reduced annotation costs by 60%

Validated on a widely used dataset

Enhanced anomaly detection accuracy

Abstract

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%.