计算机工程与应用2026，Vol.62Issue(5)：302-313,12.DOI:10.3778/j.issn.1002-8331.2411-0324
融合语义流的门控自适应交通场景语义分割算法
Gated Adaptive Traffic Scene Semantic Segmentation Algorithm Integrating Semantic Stream
摘要
Abstract
Focusing on the existing traffic scene semantic segmentation algorithms being difficult to accurately segment the object body and object edges at the same time,resulting in small objects being easily integrated into the surrounding background and discontinuous segmentation of slender stripes,a gated adaptive traffic scene semantic segmentation algo-rithm with fused semantic streams is proposed.Firstly,a parallel complementary adaptive gating module based on local and global is constructed.The complementary feature information extracted by adaptive fusion of multi-head self-atten-tion mechanism and deep convolution is used to overcome the similar feature encoding problem between different catego-ries and enhance the discriminative representation between different objects.Secondly,taking advantage of the receptive field advantage and inductive bias characteristics of convolution,a lightweight local feature extraction module based on deep separable convolution is constructed to capture local detail features of feature maps at multiple scales to enhance the network's representation ability for small objects while avoiding network structural redundancy.Finally,a semantic flow-guided cross-layer aggregation module is designed to align and aggregate adjacent feature maps under downsampling operations to further model the global context and alleviate the problem of discontinuous segmentation of thin and long objects.Experi-mental results on the traffic scene datasets Cityscapes and CamVid demonstrate that the proposed algorithm can achieve 80.16％and 84.78％mIoU,and can improve the segmentation accuracy of small objects and thin strips while refining the object segmentation edges.关键词
语义分割/深度学习/Transformer/卷积神经网络(CNN)/交通场景Key words
semantic segmentation/deep learning/Transformer/convolutional neural network(CNN)/traffic scene分类
信息技术与安全科学引用本文复制引用
谢新林,段泽云,王荃毅,谢刚..融合语义流的门控自适应交通场景语义分割算法[J].计算机工程与应用,2026,62(5):302-313,12.基金项目
国家自然科学基金(62006169) （62006169）
山西省重点研发计划基金(202202010101005) （202202010101005）
山西省基础研究计划面上项目(202303021221141) （202303021221141）
太原市关键核心技术攻关"揭榜挂帅"项目(2024TYJB0133,2024TYJB0137). （2024TYJB0133,2024TYJB0137）
