用EL图像精准识别光伏板裂纹，还能估算发电损失。
STC-Net: Electroluminescence-Based Solar Cell Crack Segmentation for Power Loss Estimation
- 结合边缘与谱先验，提升细长裂纹的连续性分割
- 在未见数据上达72.52 MIoU，裂纹定位准确
- 首次实现裂纹分割到发电损失估算的直接映射
电致发光（EL）图像中的裂纹精准评估对光伏系统可靠性分析至关重要，但现有分割方法难以捕捉裂纹细长、结构受限的特性。本文提出太阳能拓扑裂纹网络（STC-Net），融合边缘先验、谱先验及边界-拓扑精修模块，显著提升裂纹连续性与边界保真度。该框架进一步将分割结果拓展至发电损失估计，通过预测掩码构建关联裂纹的无效面积代理。在PVEL-S数据集上的实验表明，STC-Net训练阶段达到95.98 MIoU、98.01 MDice和98.00 MAcc，测试阶段在未见样本上实现72.52 MIoU和80.16 MDice。结果证明，STC-Net不仅实现高精度裂纹定位，还建立了基于EL缺陷分割与光伏退化评估之间的实用桥梁。
原文摘要 · Abstract (English)
Accurate crack assessment in electroluminescence (EL) images is important for photovoltaic (PV) reliability analysis, yet existing segmentation methods often fail to capture the thin, elongated, and structurally constrained nature of crack defects. This paper proposes a Solar Topology Crack Network (STC-Net) that incorporates edge priors, spectral priors, and a boundary-topology refinement module to improve crack continuity and boundary preservation. The framework further extends segmentation to power-loss estimation by deriving a crack-associated inactive-area proxy from the predicted masks. Experiments on the PVEL-S dataset show that STC-Net achieves 95.98 MIoU, 98.01 MDice, and 98.00 MAcc during training, and 72.52 MIoU and 80.16 MDice on unseen test samples. These results demonstrate that STC-Net provides accurate crack localization while offering a practical link between EL-based defect segmentation and PV degradation assessment.
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