Abstract
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.
Highlights
Visual inspection of solar modules using EL imaging allows to identify damage inflicted to solar panels either by environmental influences such as hail, during the assembly process, or due to prior material defects or material aging [5,10,65,90,91,93]
These 44 solar modules consist of 2,624 solar cells out of which 715 are definitely defective with defects ranging from microcracks to completely disconnected cells and mechanically induced cracks. 106 solar cells exhibit smaller defects that are not with certainty identifiable as completely defective, and 295 solar cells feature miscellaneous surface abnormalities that are no defects
The first performance metric is the Root Mean Square Error (RMSE) given in pixels between the corners of the quadrilateral mask computed from the ground truth annotations and the corners estimated by the individual modalities
Summary
Visual inspection of solar modules using EL imaging allows to identify damage inflicted to solar panels either by environmental influences such as hail, during the assembly process, or due to prior material defects or material aging [5,10,65,90,91,93]. The resulting defects can notably decrease the photoelectric conversion efficiency of the modules and their energy yield. This can be avoided by continuous inspection of solar modules and maintenance of defective units. An accurate segmentation allows to extract spatially normalized solar cell images. We already used the proposed method to develop a public dataset of solar cells images [12], which are highly accurate training data for classifiers to predict defects in solar modules [18,60]. The learned representations, are not naturally invariant to other spatial deformations such as rotation and scaling [35,44,52]
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