An End-to-End Automated Pipeline for Controllable Crack Data Synthesis

The paper introduces an end‑to‑end automated pipeline that generates controllable crack data for deep‑learning inspection. It uses procedurally sampled Bézier‑curve skeletons converted into realistic crack masks via a GAN, and a dual‑ControlNet diffusion model that separates appearance from geometry while enforcing boundary consistency. The method supports both background‑free synthesis and context‑aware inpainting, and shows improved performance over existing augmentation baselines on CRACK500 and CrackTree200 datasets.

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• diffusion