Data augmentation strategy for generating realistic samples on defect segmentation task

In the anomaly and defect detection tasks, the number of negative samples greatly exceeds the number of defective samples. As a result, a high-class imbalance exists among different classes in the detection task. In our work, we introduce a data-level solution for improving the generalization performance of the semantic segmentation of surface defects based on a data augmentation (DA) strategy. In particular, our DA approach comprised a generative stage to simulate synthetic defects and a validation stage to validate the synthetic image as close as possible to the real one. A Siamese network fully validates our synthetic samples to select only synthetic defects as close to the real ones. We demonstrated the effectiveness of our approach in a real-use case scenario to baseline DA approaches. Our DA approach allows balancing the minority classes while improving the overall generalization performance for semantic segmentation for defect detection.