基于轻量级UNet的复杂背景字符语义分割网络  

Semantic segmentation network for complex background characters based on lightweight UNet

作　　者：顾天君 孙阳光[1,2] 林虎 GU Tianjun;SUN Yangguang;LIN Hu(South-Central Minzu University,College of Computer Science,Wuhan 430074,China;South-Central Minzu University,Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises,Wuhan 430074,China)

机构地区：[1]中南民族大学计算机科学学院,武汉430074 [2]中南民族大学湖北省制造企业智能管理工程技术研究中心,武汉430074

出　　处：《中南民族大学学报（自然科学版）》2024年第2期273-279,共7页Journal of South-Central University for Nationalities：Natural Science Edition

基　　金：湖北省技术创新专项重大资助项目(2019ABA101);湖北省科技重大专项资助项目(2020AEA011);武汉市科技计划应用基础前沿资助项目(2020020601012267)。

摘　　要：针对传统复杂背景字符分割算法的不足,提出了一种基于轻量级UNet的复杂背景字符语义分割网络.网络结构基于UNet,在特征提取模块中,将传统卷积变为深度可分离卷积,减少了网络特征提取模块的参数量以及计算量,并引入残差学习模块解决网络退化问题.在自制数据集以及H-DIBCO2018公开数据集上展开实验,并与FCN8s、AttationUNet和UNet进行比较.实验结果表明:所提出的网络可同时兼顾计算效率与分割精度,具有实用性.Towards the problems of traditional complex background character segmentation algorithm,a semantic segmentation network for complex background characters based on lightweight UNet is proposed.The network structure is based on UNet.In the feature extraction module,the traditional convolution is changed into deepthwise separable convolution,which greatly reduces the number of parameters and computation of the network feature extraction module.The residual learning module is introduced to solve the network degradation problem.Experiments were performed on the self-made dataset and H-DIBCO2018 open dataset,and compared with FCN8s,AttationUNet and UNet.Experimental results show that the proposed network has both computational efficiency and segmentation accuracy,and is practical.

关 键 词：UNet网络 深度可分离卷积 残差学习模块 复杂背景 字符语义分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]