数据采集与处理2024，Vol.39Issue(6)：1505-1516,12.DOI:10.16337/j.1004-9037.2024.06.018
多级注意力特征优化的道路场景实时语义分割
Real-Time Semantic Segmentation of Road Scene Based on Multi-level Attention Feature Optimization
摘要
Abstract
Aiming at the problems of overlapping targets in complex and changeable road scenes,it is difficult to segment image edges and extract small target features.A multi-level attention feature optimization method for real-time semantic segmentation of road scenes is proposed.Firstly,a lightweight residual attention module is designed,taking into account the difference in feature weights at different levels,and optimizing local features of the image through a compressed attention mechanism,thereby improving the edge effect between pixels.Then,the channel attention and depth aggregation pyramid pooling module are designed to further strengthen the extraction of semantic context information,thereby solving the problem of small target information loss.Finally,the attention fusion module is designed to fuse feature information at different scales from top to bottom.It can achieve effective interaction of global feature information and enhance the network's expression of important features.Experimental tests are carried out on the Cityscapes and CamVid road scene datasets,and the segmentation accuracy is 74.4% and 67.7%,respectively,and the inference speed are 138 frames/s and 148 frames/s.Compared with the excellent methods in recent years,this method improves the loss of image edge information and optimizes the segmentation accuracy of small objects in the image.关键词
道路场景/语义分割/空洞卷积/注意力机制/特征融合Key words
road scene/semantic segmentation/hole convolution/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
张鹏,彭宗举,张文瑞,罗英国,韦玮,王培容..多级注意力特征优化的道路场景实时语义分割[J].数据采集与处理,2024,39(6):1505-1516,12.基金项目
国家自然科学基金(62371081) （62371081）
重庆市自然科学基金(cstc2021jcyj-msxmX0411,CSTB2022NSCQ-MSX0873). （cstc2021jcyj-msxmX0411,CSTB2022NSCQ-MSX0873）
