Semantic segmentation network based on attention mechanism and feature fusion

To address the issues of multi-scale object segmentation errors，poor correlation between multi-scale feature maps and feature maps at different stages in the DeepLabv3+ network，the following modules are proposed to incorporate，including a global context attention module，a cascade adaptive Scale awareness module，and an attention optimized fusion module. The global context attention module is embedded in the initial stage of the backbone network for feature extraction，allowing it to capture rich contextual information. The cascade adaptive scale awareness module models the dependencies between multi-scale features，enabling a stronger focus on the features relevant to the target. The attention optimized fusion module merges multiple layers of features through multiple pathways to enhance pixel continuity during decoding. The improved network is validated on the CityScapes dataset and PASCAL VOC2012 augmented dataset，and the experimental results demonstrate its ability to overcome the limitations of DeepLabv3+ . Furthermore，the mean intersection over union reaches 76.2% and 78.7% respectively. © 2025 Editorial Board of Jilin University. All rights reserved.