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arXiv · 2603.13337

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

Ojas Sanghi·Norman Jost·Benjamin G. Pierce·Emma Cooper·Isaiah H. Deane·Jennifer L. Braid
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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.

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Ojas Sanghi, Norman Jost, Benjamin G. Pierce, Emma Cooper, Isaiah H. Deane, Jennifer L. Braid. 2026-03-06. MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells. https://doi.org/10.1016/j.solener.2026.114469

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