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.

翻译：自动裂缝分割对于状态评估至关重要，但其部署受限于稀缺的像素级标注和领域偏移问题。本文提出CrackSegFlow，一种可控的流匹配合成框架，能够根据二元掩码生成对齐的图像-掩码对裂缝图像。该渲染器结合了保持拓扑结构的掩码注入与边缘门控机制，以维持细薄结构的连续性并抑制误报。一个类别条件流匹配掩码模型可合成具有可控裂缝覆盖率的掩码，从而无需人工标注即可生成平衡且拓扑多样的数据。我们将掩码注入无裂缝背景中以增加光照多样性并减少误报。在基于CNN-Transformer骨干网络的五个数据集上，引入合成图像对使域内性能提升了5.37 mIoU和5.13 F1；而利用目标掩码统计信息进行目标引导的跨域合成则实现了13.12 mIoU和14.82 F1的性能增益。我们还发布了包含5万个图像-掩码对的CSF-50K数据集用于基准测试。