A model was proposed to address issues with low segmentation accuracy， leakage of tiny cracks， and background interference in the segmentation process of concrete surface cracks. The model combined linear guidance and mesh optimization for crack segmentation. Firstly， the backbone network was enriched with a multi-branch linear guidance module. The network's ability to represent the linear structure of cracks was boosted by adaptive single-dimensional pooling. This facilitated the establishment of connections between cracks in different areas， enhanced the capability to perceive global context data， and improved the network's segmentation accuracy. Then， a module for mesh detail optimization is proposed， which divides the entire spatial domain into several spatial meshes through the three steps of partitioning， optimization， and merging. The fine cracks' information in the spatial meshes was extracted to prevent the leakage of fine cracks. Finally， a mixed attention module was embedded in the skip connections of the backbone network， highlighting crack features in the two-dimensional space and channels while also reducing background interference. On the Deepcrack537， Crack500， and CFD crack datasets， the proposed model achieves IoU values of 77.07%， 58.96%， and 56.55%， respectively. The F1-score values also performs well， achieving 87.05%， 74.19%， and 72.24%， respectively. These results are significantly better than those of most existing methods， with superior segmentation accuracy.