With the widespread application of near-infrared imaging technology in medical and industrial fields, accurate semantic segmentation has become crucial. However, existing methods still struggle with segmentation accuracy under complex backgrounds and low-contrast conditions. To address this issue, this paper proposes a near-infrared semantic segmentation method based on dual-branch structures for semantic and detail information extraction. First, the model’s feature extraction capability is enhanced by incorporating depthwise separable convolutions and establishing a dual-branch architecture to separately capture semantic and detail information. Second, a multi-scale information fusion strategy is employed to integrate features from different levels to improve segmentation accuracy. Finally, the proposed method was systematically evaluated on multiple publicly available datasets, achieving a 5.95% improvement in the Dice coefficient and an average 2.3% increase in Recall compared to existing methods, highlighting its robustness and effectiveness in practical applications.