# MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells

Source: [MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells](<https://www.sota2.com/research/paper/69b8e0080636e5bf4afbe9c5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-18T05:00:04.466Z

Authors: Ojas Sanghi, Norman Jost, Benjamin G. Pierce, Emma Cooper, Isaiah H. Deane, Jennifer L. Braid (1) \_\_INSTITUTION\_6\_\_ Sandia National Laboratories, (2) Case Western Reserve University, (3) University of Colorado, Boulder)

Published: 2026-03-06T20:48:53.000Z

## Abstract

Electroluminescence (EL) imaging is widely used to detect defects in photovoltaic (PV) modules, and machine learning methods have been applied to enable large-scale analysis of EL images. However, existing methods cannot assign multiple labels to the same pixel, limiting their ability to capture overlapping degradation features. We present a multi-channel U-Net architecture for pixel-level multi-label segmentation of EL images. The model outputs independent probability maps for cracks, busbars, dark areas, and non-cell regions, enabling accurate co-classification of interacting features such as cracks crossing busbars. The model achieved an accuracy of 98% and has been shown to generalize to unseen datasets. This framework offers a scalable, extensible tool for automated PV module inspection, improving defect quantification and lifetime prediction in large-scale PV systems.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on Solar Panel Defect Dataset (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-solar-panel-defect-dataset-test.md>): Semantic segmentation; Solar Panel Defect Dataset (test); Accuracy: 98.2.

- [Semantic Segmentation on Solar Panel Defect Dataset (5-Fold CV)](<https://www.sota2.com/research/sota/semantic-segmentation-on-solar-panel-defect-dataset-5-fold-cv.md>): Semantic segmentation; Solar Panel Defect Dataset (5-Fold CV); Mean Accuracy: 98.674.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.13337>)

[PDF](<https://arxiv.org/pdf/2603.13337.pdf>)