A learning-based approach for aluminum tube defect detection using imbalanced dataset

•Maximum mean discrepancy was employed to decide the optimal structure of the self-organizing neural network and find the overlapping area between classes adaptively.•A region-dependent processing strategy was introduced.•The proposed model has been evaluated on the imbalanced aluminum tube defect dataset compared with seven state-of-the-art models. Experimental results showed that our method had superior performance.