解决光伏图像中缺陷重叠识别难题，实现多标签像素级分割。
MultiSolSegment: Multi-channel segmentation of overlapping features in electroluminescence images of photovoltaic cells
- 设计多通道U-Net，独立输出裂纹、栅线、暗区等概率图。
- 在复杂交叠场景下准确识别裂纹穿过栅线等交互特征，准确率达98%。
- 适用于大规模光伏系统缺陷检测，可扩展至新数据集。
电致发光（EL）成像广泛用于光伏（PV）组件缺陷检测，机器学习方法已用于大规模分析EL图像。然而，现有方法无法为同一像素分配多个标签，难以捕捉重叠退化特征。本文提出一种多通道U-Net架构，实现对EL图像的像素级多标签分割。模型独立输出裂纹、栅线、暗区及非电池区域的概率图，可精准识别裂纹与栅线交叉等交互特征。模型准确率达到98%，并已在未见数据集上验证泛化能力。该框架为自动化光伏组件检测提供可扩展、可拓展的工具，提升大规模光伏系统中的缺陷量化与寿命预测精度。
原文摘要 · Abstract (English)
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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