Convolutional Neural Networks (CNN) have widely used in semantic segmentation, and can effectively extract local hierarchical information while being unsatisfactory in extracting global information. By contrast, Transformer is good at extracting long-distance dependencies in semantics while it is time-consuming. In this work, we propose a Light CNN-Transformer Dual-Branch Network (LCTDBNet) for real-time semantic segmentation. It consists of a longer CNN branch to extract local hierarchical information and a shorter Transformer branch to extract global contextual information. The CNN branch uses a lightweight encoder-decoder structure to further extract more local hierarchical information. We propose a Deep Strip Aggregation Pyramid Pooling Module (DSAPPM) to extract contextual and strip information. We further propose a Feature Pooling Refinement Module (FPRM) to optimise the feature representation at different stages. Finally, we propose a CNN-Transformer Fusion Module (CTFM) to fuse the features of two branches. Experimental results demonstrate that our proposed LCTDBNet is effective and achieves satisfactory results. Specifically, the base version of LCTDBNet achieves 80.3% mean intersection over union (mIoU) at 78.6 frames per second (FPS) on Cityscapes, 80.0% mIoU at 137.5 FPS on CamVid and 40.9% mIoU at 253.7 FPS on ADE20 K.