通信学报2025，Vol.46Issue(2)：176-190,15.DOI:10.11959/j.issn.1000-436x.2025026
基于深度学习的轻量级实时图像分割方法研究
Research on lightweight real-time image segmentation methods based on deep learning
摘要
Abstract
In response to the computational and storage burdens caused by the increasing model complexity in deep learn-ing applications,especially in image segmentation tasks where algorithmic complexity,insufficient real-time responsive-ness,and high memory usage were prevalent,a lightweight and efficient segmentation network architecture——multi-scale superposition fusion network(MSFNet)was proposed.MSFNet featured a dual-branch multi-scale boundary fu-sion module,which effectively enhanced segmentation accuracy by integrating feature information and boundary details from different scales.At the same time,it significantly reduced the model parameter count.Experimental results show that MSFNet outperforms other models on three public datasets,with a model size of only 0.6×106 parameters.On the RTX 3070 GPU,it processes 800×800 pixels images in just 12 ms,significantly improving the execution efficiency and resource utilization of segmentation tasks.Therefore,this model is particularly well-suited for deployment on resource-constrained edge or mobile devices,providing a favorable technical foundation for real-time image segmentation applica-tions.关键词
图像分割/轻量级实时网络/双分支多尺度边界融合模块Key words
image segmentation/lightweight real-time network/dual-branch multi-scale boundary fusion module分类
信息技术与安全科学引用本文复制引用
李建锋,熊明强,陈园琼,王宗达,向涛,孙培玮..基于深度学习的轻量级实时图像分割方法研究[J].通信学报,2025,46(2):176-190,15.基金项目
国家自然科学基金资助项目(No.61962023) The National Natural Science Foundation of China(No.61962023) （No.61962023）
