U2bil:a two-phase class separation method for unbalanced tunnel defects via class incremental learning

As the demand for tunnel inspection grows, visual methods are being utilized, but traditional machine learning techniques struggle with continuous learning and updating prototype classes without repeated defect data acquisition. Incremental learning provides a way to address catastrophic forgetting, but conventional methods often cannot handle the imbalanced data typical in real-world situations. This paper proposes a two-stage learning paradigm specifically designed for incremental learning to reduce overfitting from head data in unbalanced classes. Our approach maximizes symmetric separation of inter-class prototypes in the classifier space by integrating DR loss within the ETF classifier. Compared to other leading incremental learning methods, our approach shows superior performance on tunneling data.