SOURCE-LINKED INTELLIGENCE

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

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

Vision-based crack inspection depends on segmentation networks whose reliability depends on the quantity, diversity and label quality of their training data. Pixel-level annotations are costly, and crack images of specific structures are scarce. Generative augmentation can supply additional data, but existing methods address isolated steps. They reuse annotated masks, offer limited control over crack geometry, and adopt the conditioning mask as the label without checking it. This paper presents an end-to-end pipeline that produces labelled crack data without manual annotation and assesses the

Read original source ↗ Open in workspace

recordType

paper

region

Global