用生成模型分离光伏缺陷图像，提升小样本下多缺陷分类准确率
A Generative Approach for Improving Multi-Label Defect Classification in Photovoltaic Modules
- 用LaMa修复模型去除特定缺陷，生成单缺陷训练图
- 罕见缺陷F1分数最高提升63.6%，共现错误减少26%
- 适合数据少、缺陷重叠的光伏质检场景
本文针对光伏组件电致发光（EL）图像中多缺陷共现导致的分类模糊问题，提出生成式缺陷隔离方法（GDI）。通过使用带有快速傅里叶卷积的LaMa图像修复模型，移除指定缺陷并生成真实感强的单缺陷训练样本。在ViT-S、ViT-L和EfficientNetV2-L架构上进行的大量实验表明，GDI显著优于基线方法，在低数据条件下性能提升最明显；类别级分析显示，稀有缺陷的F1分数最高提升63.6%。同时，GDI有效缓解了共现缺陷带来的学习混淆，使共现分类错误率降低26%。本工作为最大化现有分割数据集价值提供了有效方案，并建立了该领域多标签分类的新基准。
原文摘要 · Abstract (English)
This paper addresses the challenge of multi-label defect classification in electroluminescence (EL) images of photovoltaic (PV) cells. Training models on images where multiple defects co-occur creates learning ambiguity, making it difficult to disentangle visual features for specific defect types, a problem compounded by the scarcity of examples for individual classes. To tackle this, we introduce Generative Defect Isolation (GDI), utilizing the LaMa inpainting model with Fast Fourier Convolutions to remove selected defects and generate realistic, single-defect training samples. Extensive experiments on Vision Transformer (ViT-S, ViT-L) and EfficientNetV2-L architectures demonstrate that GDI significantly outperforms baselines. The performance gains are most pronounced in low-data scenarios; class-wise analysis shows substantial improvements, boosting the F1-Score for rare defect classes by up to 63.6%. Furthermore, GDI effectively resolves learning ambiguity from co-occurring defects, yielding a 26% reduction in such co-occurring classification errors. Our work establishes GDI as an effective method for maximizing the value of existing segmentation datasets and sets a new performance benchmark for multi-label classification in this domain.
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