A Conditional Diffusion-Based Crack Segmentation Model: ConditionCrack Segmentation

Pavement cracks pose a direct threat to the safety of transportation systems. ConditionCrack Segmentation (CCS), a pavement crack segmentation model based on conditional diffusion, is proposed in this study. The model progressively removes noise from a random Gaussian distribution, and it is guided by image conditions to generate crack predictions. The model comprises two components: the image encoder and the map decoder. As the image encoder, the combination of MiT and FPN, aggregates multi-scale features and uses the encoded feature map as a conditional input to supplement and constrain noised labels. The map decoder uses stacked transformer layers, reducing the number of parameters in the denoising network and enhancing the ability of the decoder to model both the local details of fractures and global sequences. The model introduces Classifier-Free Guidance (CFG) and a Sampling Drift Calibration Module (SDCM) to adaptively calibrate the decoder, ensuring the effectiveness of the conditional inputs. The proposed CCS approach achieves the highest mIoU on all three evaluated public datasets (mIoU = 84.46 on CRACK500, mIoU = 89.17 on DeepCrack, and mIoU = 79.47 on CFD), outperforming both mainstream semantic segmentation models and pavement crack segmentation models using diffusion models. Moreover, the proposed CCS model achieves 32 FPS, providing a practical real-time detection system framework after performing structural optimization.