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 ridge 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 high sensitivity and a high similarity between automatically segmented and ground truth solar cell masks.

红外光谱中捕获的高分辨率电致发光（EL）图像可以目视和非破坏性地检查光伏（PV）模块的质量。然而，目前，这种目视检查需要训练有素的专家来辨别各种类型的缺陷，这既费时又昂贵。因此，细胞的自动分割是自动化外观检查工作流程的关键步骤。在这项工作中，我们提出了一种鲁棒的自动分割方法，用于从PV模块的EL图像中提取单个太阳能电池。这样就可以对大量数据进行受控研究，以了解模块随时间推移而退化的影响，这一过程尚未完全了解。所提出的方法在几个步骤中从低水平的脊边缘特征推断出高水平的太阳能模块表示。该算法的重要步骤是通过利用铅垂线约束来制定关于镜头校准的分割问题。我们在各种太阳能模块类型的数据集上评估了我们的方法，该数据集总共包含408个具有各种缺陷的太阳能电池。我们的方法以中值加权Jaccard索引为 \（94.47 \％\）和 \（F_1 \） 得分\（97.62 \％\），都表明自动分段的和真实的太阳能电池面罩之间具有很高的灵敏度和很高的相似性。