CrackSegFlow: Controllable Flow Matching Synthesis for Generalizable Crack Segmentation with a 50K Image-Mask Benchmark

Automated crack segmentation is essential for condition assessment, yet deployment is limited by scarce pixel-level labels and domain shift. We present CrackSegFlow, a controllable flow-matching synthesis framework that generates crack images conditioned on binary masks with mask-image alignment. The renderer combines topology-preserving mask injection with edge gating to maintain thin-structure continuity and suppress false positives. A class-conditional flow-matching mask model synthesizes masks with control over crack coverage, enabling balanced, topology-diverse data without manual annotation. We inject masks into crack-free backgrounds to diversify illumination and reduce false positives. On five datasets with a CNN-Transformer backbone, incorporating synthesized pairs improves in-domain performance by 5.37 mIoU and 5.13 F1, and target-guided cross-domain synthesis yields gains of 13.12 mIoU and 14.82 F1 using target mask statistics. We also release CSF-50K, 50,000 image-mask pairs for benchmarking.