STC-Net：基于电致发光的太阳能电池裂纹分割用于功率损失估计
STC-Net: Electroluminescence-Based Solar Cell Crack Segmentation for Power Loss Estimation
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中文总结 AI 辅助
STC-Net通过融合多先验与拓扑细化模块实现太阳能电池EL图像裂纹精准分割，可关联缺陷与功率损失，在PVEL-S数据集上取得优异分割性能，为光伏可靠性分析提供实用工具。
中文摘要 AI 辅助
在电致发光（EL）图像中准确评估裂纹对光伏（PV）可靠性分析至关重要，但现有分割方法往往无法捕捉裂纹缺陷纤细、细长且受结构约束的特性。本文提出Solar Topology Crack Network（STC-Net），该网络融合边缘先验、光谱先验及边界-拓扑细化模块，以提升裂纹连续性与边界保留能力。该框架还通过从预测掩码推导与裂纹相关的非活动区域代理，将分割扩展至功率损失估计。在PVEL-S数据集上的实验显示，STC-Net训练时达到95.98 MIoU、98.01 MDice和98.00 MAcc，在未见过的测试样本上达到72.52 MIoU和80.16 MDice。这些结果表明，STC-Net可实现准确的裂纹定位，同时在基于EL的缺陷分割与光伏退化评估之间建立了实用关联。
英文摘要
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.
发表机构
- RMIT University（皇家墨尔本理工大学）
- University of Jaffna（贾夫纳大学）
- Clean Energy Council（清洁能源委员会）
机构由 AI 辅助整理，请以论文原文为准。
