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

2026-09-11

AI Summary

该论文提出了一种端到端自动化管道，通过GAN生成可控裂纹数据，并利用双ControlNet框架实现形态和边界的一致性，解决了现有方法在裂纹合成中控制不足的问题。

Abstract

Automated crack inspection increasingly relies on deep learning, yet its reliability is limited by scarce and weakly controllable defect data. Existing generative augmentation methods often treat crack synthesis as a generic image-generation task, offering insufficient control over morphology, boundary fidelity, and scene context. This paper proposes an end-to-end automated pipeline for controllable crack data synthesis that formalizes crack geometry and inspection context into reusable computational constraints. First, procedurally sampled Bézier-curve skeletons are translated into realistic crack masks using a GAN, enabling scalable generation of diverse crack morphologies without manual mask design. Second, a dual-ControlNet diffusion framework disentangles appearance guidance from geometric guidance, with an edge-based branch enforcing strict boundary consistency. The framework supports both background-free synthesis and context-aware inpainting. Experiments on CRACK500 and CrackTree200 show consistent gains over existing augmentation baselines, demonstrating a scalable engineering informatics workflow for automated crack-inspection data generation.

Problem

• crack synthesis
• deep learning
• data augmentation
• geometry control
• boundary fidelity

Innovation

• end-to-end automated pipeline
• controllable crack data synthesis
• GAN
• dual-ControlNet diffusion framework
• boundary consistency