Diffusion-Driven Generation of Synthetic Complex Concrete Crack Images for Segmentation Tasks

Crack detection accuracy in computer vision is often constrained by limited annotated datasets. Although Generative Adversarial Networks (GANs) have been applied for data augmentation, they frequently introduce blurs and artifacts. To address this challenge, this study leverages Denoising Diffusion Probabilistic Models (DDPMs) to generate high-quality synthetic crack images, enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation. The proposed framework involves a two-stage pipeline: first, DDPMs are used to synthesize high-fidelity crack images that capture fine structural details. Second, these generated samples are combined with real data to train segmentation networks, thereby improving accuracy and robustness in crack detection. Compared with GAN-based approaches, DDPM achieved the best fidelity, with the highest Structural Similarity Index (SSIM) (0.302) and lowest Learned Perceptual Image Patch Similarity (LPIPS) (0.461), producing artifact-free images that preserve fine crack details. To validate its effectiveness, six segmentation models were tested, among which LinkNet consistently achieved the best performance, excelling in both region-level accuracy and structural continuity. Incorporating DDPM-augmented data further enhanced segmentation outcomes, increasing F1 scores by up to 1.1% and IoU by 1.7%, while also improving boundary alignment and skeleton continuity compared with models trained on real images alone. Experiments with varying augmentation ratios showed consistent improvements, with F1 rising from 0.946 (no augmentation) to 0.957 and IoU from 0.897 to 0.913 at the highest ratio. These findings demonstrate the effectiveness of diffusion-based augmentation for complex crack detection in structural health monitoring.