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

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. • Developed multi-channel U-Net for pixel-level multi-label segmentation of EL images. • New model enables co-classification of overlapping PV degradation features. • Prepared a diverse set of labeled 2,340 mono-Si images, with categories: dark-area, crack, busbar and no cell. • Bench-marked against another widely used segmentation model for solar cells with better results.