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%.

该研究提出一种结合最新的数据驱动技术的方法，用于自动检测光伏电池电子发光图像中的异常，并生成高级注释以降低标注成本。该方法具有适应各种光伏电池类型、成本效益高的微调和利用公共数据集生成高级标注的特点。通过在广泛使用的数据集中进行注释，标注成本降低了 60%。

一种可扩展的架构，用于在电致发光图像中标注光伏电池缺陷

A scalable framework for annotating photovoltaic cell defects in electroluminescence images

Electroluminescence (EL) imaging is a useful modality for the inspection of
photovoltaic (PV) modules. EL images provide high spatial resolution, which
makes it possible to detect even finest defects on the surface of PV modules.
However, the analysis of EL images is typically a manual process that is
expensive, time-consuming, and requires expert knowledge of many different
types of defects. In this work, we investigate two approaches for automatic
detection of such defects in a single image of a PV cell. The approaches differ
in their hardware requirements, which are dictated by their respective
application scenarios. The more hardware-efficient approach is based on
hand-crafted features that are classified in a Support Vector Machine (SVM). To
obtain a strong performance, we investigate and compare various processing
variants. The more hardware-demanding approach uses an end-to-end deep
Convolutional Neural Network (CNN) that runs on a Graphics Processing Unit
(GPU). Both approaches are trained on 1,968 cells extracted from high
resolution EL intensity images of mono- and polycrystalline PV modules. The CNN
is more accurate, and reaches an average accuracy of 88.42%. The SVM achieves a
slightly lower average accuracy of 82.44%, but can run on arbitrary hardware.
Both automated approaches make continuous, highly accurate monitoring of PV
cells feasible.

研究使用基于手工特征和基于深度卷积神经网络（CNN）的两种方法，在高分辨率电致发光（EL）图像中自动检测光伏电池的缺陷，其中 CNN 方法准确率达到 88.42％。