High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kinds of defects, which is time-consuming and expensive. Automated segmentation of cells is therefore a key step in automating the visual inspection workflow. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. This enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an $F_1$ score of 97.62%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks.
翻译:红外线光谱中捕获的高分辨率电光素(EL)图像允许对光伏(PV)模块的质量进行视觉和非破坏性的检查。但目前,这种视觉检查需要经过培训的专家来辨别不同种类的缺陷,这些缺陷耗时费钱。因此,细胞的自动分解是使视觉检查工作流程自动化的关键步骤。在这项工作中,我们建议了一种强大的自动分解方法,用于从光电池模块的EL图像中提取单个太阳电池。这样就可以对大量数据进行控制性研究,了解模块降解在时间-一个尚未完全理解的进程中的影响。提议的方法在几个步骤中推断出一个来自低水平边缘特征的高水平太阳能模块。算法的一个重要步骤是通过利用液压限制来形成透镜校准的分解问题。我们评估了我们关于包含总共408个有各种缺陷的太阳电池的各种太阳单元的数据集的方法。我们的方法以94.47%的中位加权 Jacard指数和97.62%的美元分数来强有力地解决这项任务。