A Robust Self-supervised Approach For Fine-grained Crack Detection In Concrete Structures

This article discusses the use of computer vision and self-supervised learning techniques to detect and segment fine-grained cracks in concrete structures. The proposed approach, SS-YOLO, utilizes attention modules and pseudo-labeling techniques to achieve high accuracy and efficiency. Experimental results show significant improvements compared to other methods.