Abstract: Traditional segmentation algorithms fail to effectively handle complex texture backgrounds and diverse defect shapes in steel surface defect segmentation, resulting in insufficient ...

Abstract: Deep supervised learning algorithms typically require a large volume of labeled data to achieve satisfactory performance. However, the process of collecting and labeling such data can be ...