论文标题
用于注释电致发光图像中光伏细胞缺陷的可扩展框架
A scalable framework for annotating photovoltaic cell defects in electroluminescence images
论文作者
论文摘要
光伏（PV）细胞的正确功能对于确保太阳能电厂的最佳性能至关重要。 PV细胞的异常检测技术可在操作和维护（O＆M）中节省大量成本（O＆M）。最近的研究集中于自动检测电致发光（EL）图像异常的深度学习技术。自动化的异常注释可以改善当前的O＆M方法，并有助于开发决策系统以扩展PV细胞的生命周期并预测故障。本文通过提出了最先进的数据驱动技术来创建黄金标准基准的结合来解决文献中缺乏异常分割注释。提出的方法突出了（1）对新的PV单元类型，（2）经济高效的微调和（3）利用公共数据集生成高级注释的适应性。该方法已在广泛使用的数据集的注释中得到了验证，将注释成本降低了60％。
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%.
