现代电子技术2025，Vol.48Issue(7)：29-34,6.DOI:10.16652/j.issn.1004-373x.2025.07.005
基于改进HRNet和PPM的图像语义分割方法的研究
Research on image semantic segmentation method based on improved HRNet and PPM
摘要
Abstract
A semantic segmentation method is proposed to address the issue of the existing semantic segmentation models being unable to balance global semantic information and local detail information,and the poor ability of residual module detail feature extraction.On the basis of the HRNet,a pyramid pooling module is introduced to balance global semantic information and local detail information.At the same time,the large-kernel deep convolution is introduced on the basis of the original residual module Basic Block,so as to improve the detail feature extraction ability of the model and improve the model accuracy significantly.Experiments on the PASCAL VOC2012 image dataset show that in comparison with the other segmentation networks,for instance,the original HRNet,the proposed algorithm achieves a significant improvement in segmentation accuracy,with an average accuracy of 89.27%.The effectiveness of each designed module has also been verified by ablation experiments,especially the improvement of Basic Block,which plays a crucial role in improving segmentation performance.This model further improves the accuracy of image semantic segmentation and achieves a more efficient,stable,and universal multi-scale semantic segmentation algorithm.关键词
HRNet/金字塔池化模块/大核深度卷积/残差模块/语义分割/深度学习Key words
HRNet/pyramid pooling module/large-kernel deep convolution/residual module/semantic segmentation/deep learning分类
电子信息工程引用本文复制引用
师佳琪,杨皓浚,刘晓悦,陈鑫..基于改进HRNet和PPM的图像语义分割方法的研究[J].现代电子技术,2025,48(7):29-34,6.基金项目
河北省重点基金项目(SJMYF202401) （SJMYF202401）
国家自然科学基金项目(42274056) （42274056）
