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Aiming at the problems of inaccurate large target segmentation and loss of small target information in current real-time semantic segmentation methods, this paper proposed a real-time semantic segmentation algorithm based on multi-branch networks. First of all, this paper optimized the bilateral segmentation network, and designed pyramid branches to expand the receptive field to cover large objects in the field of view and fully combine context information; secondly, designed a bilateral guidance fusion module to map deep and shallow features Provide guidance information to make up for the loss of small target information. Finally, this paper verified the proposed method on the Cityscapes dataset. The experimental results show that the proposed model achieves an average intersection ratio of 77.8% at an inference speed of 51.3 FPS, and the accuracy is increased by 2.5 percentage points compared with the baseline. The proposed method adopts the pyramid branch to obtain the characteristics of semantic edge regions at different scales while expanding the receptive field, and enhance the modeling ability of semantic boundaries, and the proposed bilateral guidance fusion module can more effectively integrate features of different levels, Compensating for the information loss caused by downsampling can better guide model learning. [ABSTRACT FROM AUTHOR]