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

• URL: http://arxiv.org/abs/2603.13337v1
• Date: Fri, 06 Mar 2026 20:48:53 GMT
• Title: MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells
• Abstract summary: We present a multi-channel U-Net architecture for pixel-level multi-label segmentation of Electroluminescence (EL) images.<n>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.<n>This framework offers a scalable, tool for automated PV module inspection, improving defect quantification and lifetime prediction in large-scale PV systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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