Pavement crack segmentation is important for road infrastructure inspection, but practical deployment remains challenging because many high-performance deep learning models require substantial computational resources. This issue is particularly critical in large-scale Mobile Mapping System (MMS)-based workflows, where large volumes of road surface images must be processed efficiently. To address this problem, this study proposes a lightweight CNN-Mamba hybrid architecture for crack segmentation as a deployment-oriented redesign of CT-CrackSeg. The proposed method replaces the original MobileViT-based global modelling modules with EfficientViM-inspired blocks based on hidden-state mixer-based state space duality (HSM-SSD), while preserving the overall encoder-decoder structure and refining the boundary enhancement branch with DCNv2-based deformable convolution. Experiments on the publicly available GAPs384 and CamCrack789 datasets show that the proposed model maintains competitive topology-aware segmentation performance while substantially improving computational efficiency. Compared with CT-CrackSeg, the proposed model increases inference speed from 1.49 to 4.44 FPS on GAPs384 and from 1.32 to 3.92 FPS on CamCrack789, while reducing peak memory consumption from 2827 MB to 355 MB on both datasets. At the same time, the clDice score remains comparable, changing from 0.760 to 0.758 on GAPs384 and from 0.921 to 0.922 on CamCrack789. These results indicate that the proposed architecture provides a favourable balance between crack segmentation quality and deployment efficiency, making it a practical option for large-scale pavement inspection and photogrammetric infrastructure monitoring.