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％。