Robust crack segmentation has a crucial role in automated infrastructure inspection as well as structural health monitoring. In this paper, we introduce a novel dual-branch hybrid architecture that effectively integrates local texture representation and global contextual modeling, named CBiF-Net, for robust crack segmentation. The proposed framework consists of a CNN branch for capturing fine-grained local details and a parallel Transformer branch for modeling long-range dependencies. To tightly couple the complementary features from the two branches, we introduce a Bi-linear Interaction Module (BiFusion), which enables effective cross-branch feature interaction and significantly enhances representational capability. Extensive experiments tested on the three benchmark datasets, including DeepCrack, SteelCrack, and CrackVision12k, show that our CBiF-Net consistently achieves SOTA performance in metrics of F1-score and mean Intersection-over-Union. Moreover, by adopting the lightweight MobileNetV3-Large backbone, the proposed model achieves an excellent trade-off between accuracy and computational costs in segmentation. This makes it highly suitable for real-time and large-scale practical deployment in automated crack inspection systems.