Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images

TL;DR

This paper presents a robust automated method for segmenting individual photovoltaic cells in uncalibrated electroluminescence images, facilitating large-scale analysis of module degradation without expert intervention.

Contribution

The authors introduce a novel segmentation approach that leverages lens calibration and edge features, improving accuracy in uncalibrated EL images of PV modules.

Findings

Achieved a median weighted Jaccard index of 94.47%

Attained an F1 score of 97.62%

Successfully segmented 408 solar cells with various defects

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 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.