Automated crack segmentation is essential for scalable condition assessment of pavements and civil infrastructure, yet practical deployment is limited by scarce pixel-level labels and severe domain shift across sensors, illumination, textures, and annotation conventions. This paper presents CrackSegFlow, a controllable flow-matching synthesis framework that generates photorealistic crack images conditioned on binary masks while preserving strict mask-image alignment. The generator combines topology-preserving mask injection with boundary-gated modulation to maintain thin-structure continuity and suppress texture-driven false positives. A second class-conditional flow-matching model synthesizes crack masks with explicit control over crack coverage, enabling balanced, topology-diverse paired data without additional manual annotation. We further inject crack masks into crack-free backgrounds to diversify illumination and surface artifacts and reduce false positives caused by shadows, joints, and pavement markings. Experiments on five benchmarks spanning four asphalt datasets and the crack class of a concrete-domain dataset demonstrate consistent improvements under an established hybrid CNN--Transformer segmentation backbone and a fixed training protocol. With real plus synthesized pairs, in-domain performance improves on average by 5.37 mIoU and 5.13 F1, and target-guided cross-domain synthesis yields average gains of 13.12 mIoU and 14.82 F1 using only limited target mask statistics. Compared with diffusion-based semantic synthesis, CrackSegFlow provides substantially faster deterministic sampling and improves fidelity and mask-image alignment for thin-structure crack geometry. Finally, we release CSF-50K, a public dataset of 50,000 paired crack images and pixel-accurate masks for large-scale benchmarking of generalizable crack segmentation.

翻译：自动化裂缝分割对于路面及土木基础设施的可扩展状态评估至关重要，然而实际部署受到像素级标注稀缺以及传感器、光照、纹理和标注规范间严重域偏移的限制。本文提出CrackSegFlow，一种可控的流匹配合成框架，能够基于二值掩码生成逼真的裂缝图像，同时保持严格的掩码-图像对齐。该生成器结合了保持拓扑结构的掩码注入与边界门控调制机制，以维持细薄结构的连续性并抑制纹理驱动的误检。第二个类别条件流匹配模型可合成具有明确裂缝覆盖率控制的裂缝掩码，从而无需额外人工标注即可生成均衡且拓扑多样的配对数据。我们进一步将裂缝掩码注入无裂缝背景中，以增加光照和表面伪影的多样性，并减少由阴影、接缝和路面标记引起的误检。在涵盖四个沥青数据集和一个混凝土领域数据集裂缝类别的五个基准测试上的实验表明，在既有的混合CNN-Transformer分割主干网络和固定训练协议下，该方法取得了持续的性能提升。使用真实与合成配对数据后，域内性能平均提升5.37 mIoU和5.13 F1；而仅利用有限目标掩码统计信息的目标引导跨域合成，则实现了平均13.12 mIoU和14.82 F1的性能增益。与基于扩散的语义合成方法相比，CrackSegFlow提供了显著更快的确定性采样速度，并在细薄结构裂缝几何形状的保真度和掩码-图像对齐方面表现更优。最后，我们发布了CSF-50K公开数据集，包含50,000张配对的裂缝图像及像素级精确掩码，用于可泛化裂缝分割的大规模基准测试。