CrackDiffusion: a two-stage semantic segmentation framework for pavement crack combining unsupervised and supervised processes - Han, Chengjia (2024)
Achieving precise and reliable automated pavement crack detection using deep learning techniques is vital for intelligent pavement maintenance. This study proposes CrackDiffusion, an enhanced-supervised detection framework for pavement crack, combining two supervised and unsupervised stages. In Stage 1, a multi-blur-based cold diffusion anomaly detection model is proposed, which transforms crack-containing images into crack-free images, while simultaneously extracting pixel-level crack features using the Structural Similarity Index measure (SSIM). In Stage 2, an improved supervised U-Net segmentation model enhances accuracy and robustness by building upon the unsupervised results from Stage 1, ultimately producing highly accurate pixel-level segmentation results for cracks. On four public datasets, both the proposed multi-blur-based cold diffusion model and the comprehensive CrackDiffusion framework attained the highest Intersection over Union (IoU) scores, surpassing the IoU scores of the current state-of-the-practice unsupervised and supervised segmentation models.