CrackdiffNet: A Novel Diffusion Model for Crack Segmentation and Scale-Based Analysis

Deep learning has made remarkable progress in the field of crack segmentation, particularly in handling large-scale datasets and complex images, owing to the substantial computational power currently available. However, existing methods still face significant challenges when processing images with low contrast, fine cracks, or strong noise interference. This paper introduces a novel semantic diffusion model capable of generating synthetic crack images from segmentation masks. The proposed model outperforms state-of-the-art semantic synthesis models across multiple benchmark datasets, demonstrating enhanced crack segmentation performance in complex backgrounds and addressing a critical challenge in engineering crack detection. Additionally, a new crack width calculation method is proposed, which further optimizes the measurement accuracy of crack width by leveraging the medial axis of the segmentation mask, thereby improving the model’s ability to describe crack morphology. To comprehensively evaluate the model’s performance, the dataset was categorized, and a detailed analysis of crack width errors was conducted for different regions. Specifically, the median and interquartile range (IQR) of width errors were calculated for four distinct regions: the central wall, corner edges, oblique intersections, and wall and column surfaces. Experimental results demonstrate that the proposed model excels in all regions, particularly in complex areas such as corner edges and oblique intersections, where the error is significantly lower than that of existing methods. These innovations collectively advance crack segmentation technology and provide a new solution for efficient crack detection in practical applications.