计算机与数字工程2024，Vol.52Issue(4)：1014-1020,1027,8.DOI:10.3969/j.issn.1672-9722.2024.04.010
城市场景分割的多尺度感知融合网络研究
Research of Multi-Scale Perceptual Fusion Network for Urban Scene Segmentation
摘要
Abstract
In order to solve the problem of multi-scale transformation of road scene and adapt to the requirements of automatic driving semantic scene,and reduce the complexity of the whole structure of convolutional neural network model,this paper propos-es a multi-scale perceptual fusion semantic segmentation network based on asymmetric network structure of decoder to segment road image.According to the idea of residual network and space convolution,a new Res-SS residual module is designed to improve the efficiency of feature acquisition.The multi-scale perceptual fusion extraction module is designed and adopted to extract more multi-scale feature information from different receptive fields for weighted fusion,so as to improve the robustness of the network.Be-cause the edge information of the segmented object is lost in the process of feature extraction,a Superpixel segmentation module is used to fuse the low-level information with the high-level information,so as to recover the lost information of the feature map.Exper-iments on Cityscapes dataset show that the algorithm has higher accuracy and robustness than the existing semantic segmentation al-gorithms.关键词
语义分割/卷积神经网络/残差模块/多尺度特征/特征融合/边缘信息Key words
semantic segmentation/convolutional neural network(CNN)/residual module/multi-level features/feature fu-sion/edge information分类
信息技术与安全科学引用本文复制引用
戴伟东,姜文刚..城市场景分割的多尺度感知融合网络研究[J].计算机与数字工程,2024,52(4):1014-1020,1027,8.基金项目
国家自然科学基金项目(编号:61671222)资助. （编号:61671222）
