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

Journal

Publisher: arXiv

Book Volume: 32

Article Number: 84

URI: https://arxiv.org/pdf/1806.06530.pdf

DOI: 10.1007/s00138-021-01191-9

Open Access Link: https://link.springer.com/article/10.1007/s00138-021-01191-9

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 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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How to cite

APA:

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. https://doi.org/10.1007/s00138-021-01191-9

MLA:

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

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