A Generative Approach for Improving Multi-Label Defect Classification in Photovoltaic Modules

Researchers introduced Generative Defect Isolation (GDI), a method using the LaMa inpainting model with Fast Fourier Convolutions to generate realistic single-defect training samples for multi-label defect classification in electroluminescence (EL) images of photovoltaic (PV) cells. Experiments on Vision Transformer (ViT-S, ViT-L) and EfficientNetV2-L architectures showed GDI significantly outperforms baselines, boosting F1-Score for rare defect classes by up to 63.6% and reducing co-occurring classification errors by 26%.

arXiv:2608.12725v1 Announce Type: new Abstract: 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.