空军工程大学学报2025，Vol.26Issue(2)：62-70,9.DOI:10.3969/j.issn.2097-1915.2025.02.008
基于双分支融合的图像实时语义分割方法
A Real-Time Image Semantic Segmentation Method Based on Dual Branch Fusion
摘要
Abstract
Aimed at the problems that faulty classification and incomplete segmentation are in existence in segmenting multi-scale objects to the existing real-time semantic segmentation networks,a real-time se-mantic image segmentation method is proposed based on dual branch fusion.The method introduces a scale attention fusion module that is able to fuse object spatial feature and semantic information extracted from the detail branch and semantic branch,thereby improving the accuracy of the network for multi-scale object recognition.The edge loss function is used to guide the detail branch into learning the object edge contour,improving the network's segmentation performance on object edge details.Finally,a global per-ception module is constructed to enhance the global context perception capability of the network.The ex-perimental results demonstrate that the proposed method achieves the mean Intersection over union(mIoU)of 78.1%and 76.2%on the CityScapes and CamVid datasets respectively.Additionally,the mean pixel accuracy(mPA)is 87.6%and 85.4%,respectively.For small-scale object edges,there is a more accurate segmentation,coming up to the real-time requirements on a single GTX 1080Ti GPU,and frames per second(FPS)achieves 59.8 and 43.5 respectively.关键词
深度学习/实时语义分割/尺度注意/特征融合/全局感知Key words
deep learning/real-time semantic segmentation/scale attention/feature fusion/global perception分类
计算机与自动化引用本文复制引用
宋玉琴,娄辉,张琪,商纯良..基于双分支融合的图像实时语义分割方法[J].空军工程大学学报,2025,26(2):62-70,9.基金项目
中国纺织工业联合会科技指导性项目(2019062) （2019062）
陕西省教育厅专项科研计划项目(18JK0358) （18JK0358）
