Convolutional neural networks have demonstrated efficacy in acquiring local features and spatial details
however, they struggle to obtain global information, which could potentially compromise the segmentation of important regions of an image. Transformer can increase the expressiveness of pixels by establishing global relationships between them. Moreover, some transformer-based self-attentive methods do not combine the advantages of convolution, which makes the model require more computational parameters. This work uses both Transformer and CNN structures to improve the relationship between image-level regions and global information to improve segmentation accuracy and performance in order to address these two issues and improve the semantic segmentation results at the same time. We first build a Feature Alignment Module (FAM) module to enhance spatial details and improve channel representations. Second, we compute the link between similar pixels using a Transformer structure, which enhances the pixel representation. Finally, we design a Pyramid Convolutional Pooling Module (PCPM) that both compresses and enriches the feature maps, as well as determines the global correlations among the pixels, to reduce the computational burden on the transformer. These three elements come together to form a transformer-based semantic segmentation feature fusion network (FFTNet). Our method yields 82.5% mIoU, according to experimental results based on the Cityscapes test dataset. Furthermore, we conducted various visualization tests using the Pascal VOC 2012 and Cityscapes datasets. The results show that our approach outperforms alternative approaches.