计算机与现代化Issue(8)：49-53,5.DOI:10.3969/j.issn.1006-2475.2024.08.009
基于轻量化的视频帧场景语义分割方法
Semantic Segmentation of Video Frame Scene Based on Lightweight
摘要
Abstract
Scene segmentation is crucial for computers to understand the road environment,the large semantic segmentation model based on deep learning can often achieve excellent segmentation performance,but it cannot be flexibly deployed on edge devices because of its large number of parameters and computation.To solve this problem,this paper proposes an efficient scene semantic segmentation model E-SegNet from the perspective of lightweight.Firstly,the lightweight feature extraction model EfficientNet-B0 is used as the encoder of the model to extract the hierarchical features.Then,CPAM and CCAM modules based on the self-attention mechanism are used to establish the dependency between the single element in the deep features and the global central element in the two dimensions of spatial and channel.Finally,the feature of deep and shallow layers are fused and the final prediction results are output.Experimental results on video frame data set Camseq01 show that the proposed E-SegNet model achieves better segmentation performance with less than 1/10 of the parameters of DeeplabV3+model and about 1/4 of the computational effort,which reflects the effectiveness of the model,and provides more schemes for deploying lightweight models on edge devices.关键词
深度学习/轻量级/场景分割/空间注意力/通道注意力Key words
deep learning/lightweight/scene segmentation/spatial attention/channel attention分类
信息技术与安全科学引用本文复制引用
时现伟,范鑫..基于轻量化的视频帧场景语义分割方法[J].计算机与现代化,2024,(8):49-53,5.基金项目
新疆维吾尔自治区重点研发项目(2021B01002) （2021B01002）
