Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images

Deitsch S, Buerhop-Lutz C, Sovetkin E, Steland A, Gallwitz F, Maier A, Rieß C (2021)

Publication Language: English

Publication Status: In review

Publication Type: Journal article, Original article

Future Publication Type: Journal article

Publication year: 2021


Publisher: arXiv

Book Volume: 32

Article Number: 84


DOI: 10.1007/s00138-021-01191-9

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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 kind of defects, which is time-consuming and expensive. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. Automated segmentation of cells is a key step in automating the visual inspection workflow. It also 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 F1 score of 97.62%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks.

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Deitsch, S., Buerhop-Lutz, C., Sovetkin, E., Steland, A., Gallwitz, F., Maier, A., & Rieß, C. (2021). Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images. Machine Vision and Applications, 32.


Deitsch, Sergiu, et al. "Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images." Machine Vision and Applications 32 (2021).

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