Abstract: To improve local detail recovery and overall structural representation capabilities in tobacco leaf vein segmentation, a collaborative segmentation network for tobacco leaf veins with main vein guidance and detail enhancement (DiffUNet-VEUNet, DVNet), was constructed. DVNet guided the structural modeling of the vein segmentation network by treating the main vein as a high-confidence structural prior, and strengthened the feature representation of fine veins by incorporating detail enhancement mechanisms. The results showed that the constructed DVNet achieved superior segmentation performance compared with existing mainstream methods for tobacco leaf vein segmentation. While maintaining the stability of the main vein structure, DVNet improved the recognition capability of fine veins and further enhanced the overall segmentation quality. Furthermore, qualitative visualization analysis and ablation experiments further verified the effectiveness of each core module. Overall, DVNet achieves more stable and refined tobacco leaf vein segmentation.