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This paper introduces Generative Defect Isolation (GDI), a novel approach that leverages the LaMa inpainting model with Fast Fourier Convolutions to enhance multi-label defect classification in electroluminescence images of photovoltaic cells. By generating realistic single-defect training samples, GDI effectively addresses the learning ambiguity caused by co-occurring defects and the scarcity of individual class examples. Experimental results show that GDI significantly improves classification performance, particularly in low-data scenarios, achieving up to a 63.6% increase in F1-Score for rare defect classes and reducing co-occurring classification errors by 26%.
GDI transforms defect classification by generating single-defect samples, leading to a remarkable 63.6% boost in F1-Score for rare defects.
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.
