How to address imbalance datasets in solar panel dust detection

Researchers from South Korea improved solar panel dust detection by using SMOTE and stable diffusion (SD) augmentation, with SD boosting detection accuracy from 76.5% to 98.9% while preserving spatial realism.A research group from South Korea's Jeju National University addressed dataset imbalance in photovoltaic modules by employing two complementary augmentation strategies. Specifically, they applied a feature-level synthetic minority over-sampling technique (SMOTE) and image-level stable diffusion (SD) augmentation. Dataset imbalance occurs when one class in a dataset contains far more examples ...

Den vollständigen Artikel lesen ...