Semi-supervised learning for concrete defect segmentation from images

Abstract

Deep learning-based concrete defect detection has emerged as a promising technology to overcome the limitations of manual visual inspection, which is often subjective, laborious, and time-consuming. However, most existing methods rely heavily on manual labels and adopt a fully supervised learning approach, which hinders their practical application in engineering. To tackle these concerns, we proposed a semi-supervised learning model, termed Semi-SegDefect. Our proposal utilizes a mean teacher architecture to assign pseudo-labels to the pixels of unlabeled images and performs pixel-level contrastive learning on a sparse set of hard negative pixels to achieve segmentation boundaries with the highest possible accuracy. Test results demonstrate that Semi-SegDefect outperforms fully supervised models significantly, especially when trained with limited labeled data. This method shows great promise for enhancing the accuracy and scalability of concrete defect segmentation and can contribute to the advancement of the construction industry.