Semantic segmentation network with multi-path structure, attention reweighting and multi-scale encoding

Semantic segmentation is an active field of computer vision. It provides semantic information for many applications. In semantic segmentation tasks, spatial information, context information, and high-level semantic information play an important role in improving segmentation accuracy. In this paper, a semantic segmentation network with multi-path structure, attention reweighting, and multi-scale encoding structure is proposed. Firstly, three parallel structures were designed, including a pyramid spatial path with a pyramid image input, a context path composed of a lightweight backbone network, and a semantic graph path composed of spatial graph convolutional layers. Secondly, a feature fusion module was designed to perform a weighted fusion of the output features of different paths based on the channel attention mechanism. Then, the semantic segmentation dataset CamVid and Cityscapes were used for network training. Finally, ablation experiments were carried out to verify the effectiveness of the proposed network components, and analyze the computational efficiency and segmentation accuracy of the model. The experimental results show that the semantic segmentation network can improve the accuracy of semantic segmentation by combining multi-scale information, high-level semantic information, and global context information while ensuring high computational efficiency.