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

ArXi:2603.13337v1 Announce Type: cross 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.