多尺度特征提取与融合网络——无人机图像语义分割技术研究

Q3 Decision Sciences
Xiaogang Li;Di Su;Dongxu Chang;Jiajia Liu;Liwei Wang;Zhansheng Tian;Shuxuan Wang;Wei Sun
{"title":"多尺度特征提取与融合网络——无人机图像语义分割技术研究","authors":"Xiaogang Li;Di Su;Dongxu Chang;Jiajia Liu;Liwei Wang;Zhansheng Tian;Shuxuan Wang;Wei Sun","doi":"10.13052/jicts2245-800X.1115","DOIUrl":null,"url":null,"abstract":"Since UAV aerial images are usually captured by UAVs at high altitudes with oblique viewing angles, the amount of data is large, and the spatial resolution changes greatly, so the information on small targets is easily lost during segmentation. Aiming at the above problems, this paper presents a semantic segmentation method for UAV images, which introduces a multi-scale feature extraction and fusion module based on the encoding-decoding framework. By combining multi-scale channel feature extraction and multi-scale spatial feature extraction, the network can focus more on certain feature layers and spatial regions when extracting features. Some invalid redundant features are eliminated and the segmentation results are optimized by introducing global context information to capture global information and detailed information. Moreover, one compares the proposed method with FCN-8s, MSDNet, and U-Net network models on the large-scale multi-class UAV dataset UAVid. The experimental results indicate that the proposed method has higher performance in both MIoU and MPA, with an overall improvement of 9.2% and 8.5%, respectively, and its prediction capability is more balanced for both large-scale and small-scale targets.","PeriodicalId":36697,"journal":{"name":"Journal of ICT Standardization","volume":"11 1","pages":"97-116"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/10251929/10261463/10261466.pdf","citationCount":"1","resultStr":"{\"title\":\"Multi-scale Feature Extraction and Fusion Net: Research on UAVs Image Semantic Segmentation Technology\",\"authors\":\"Xiaogang Li;Di Su;Dongxu Chang;Jiajia Liu;Liwei Wang;Zhansheng Tian;Shuxuan Wang;Wei Sun\",\"doi\":\"10.13052/jicts2245-800X.1115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since UAV aerial images are usually captured by UAVs at high altitudes with oblique viewing angles, the amount of data is large, and the spatial resolution changes greatly, so the information on small targets is easily lost during segmentation. Aiming at the above problems, this paper presents a semantic segmentation method for UAV images, which introduces a multi-scale feature extraction and fusion module based on the encoding-decoding framework. By combining multi-scale channel feature extraction and multi-scale spatial feature extraction, the network can focus more on certain feature layers and spatial regions when extracting features. Some invalid redundant features are eliminated and the segmentation results are optimized by introducing global context information to capture global information and detailed information. Moreover, one compares the proposed method with FCN-8s, MSDNet, and U-Net network models on the large-scale multi-class UAV dataset UAVid. The experimental results indicate that the proposed method has higher performance in both MIoU and MPA, with an overall improvement of 9.2% and 8.5%, respectively, and its prediction capability is more balanced for both large-scale and small-scale targets.\",\"PeriodicalId\":36697,\"journal\":{\"name\":\"Journal of ICT Standardization\",\"volume\":\"11 1\",\"pages\":\"97-116\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/iel7/10251929/10261463/10261466.pdf\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of ICT Standardization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10261466/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Decision Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of ICT Standardization","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10261466/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Decision Sciences","Score":null,"Total":0}
引用次数: 1

摘要

由于无人机航拍图像通常是由无人机在倾斜视角的高空拍摄的,数据量大,空间分辨率变化大,因此在分割过程中很容易丢失小目标的信息。针对上述问题,本文提出了一种无人机图像的语义分割方法,该方法引入了基于编解码框架的多尺度特征提取与融合模块。通过将多尺度通道特征提取和多尺度空间特征提取相结合,网络在提取特征时可以更多地关注某些特征层和空间区域。通过引入全局上下文信息来捕获全局信息和详细信息,消除了一些无效的冗余特征,并对分割结果进行了优化。此外,在大型多类无人机数据集UAVid上,将所提出的方法与FCN-8s、MSDNet和U-Net网络模型进行了比较。实验结果表明,该方法在MIoU和MPA中都具有更高的性能,总体性能分别提高了9.2%和8.5%,并且对大尺度和小尺度目标的预测能力更加平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-scale Feature Extraction and Fusion Net: Research on UAVs Image Semantic Segmentation Technology
Since UAV aerial images are usually captured by UAVs at high altitudes with oblique viewing angles, the amount of data is large, and the spatial resolution changes greatly, so the information on small targets is easily lost during segmentation. Aiming at the above problems, this paper presents a semantic segmentation method for UAV images, which introduces a multi-scale feature extraction and fusion module based on the encoding-decoding framework. By combining multi-scale channel feature extraction and multi-scale spatial feature extraction, the network can focus more on certain feature layers and spatial regions when extracting features. Some invalid redundant features are eliminated and the segmentation results are optimized by introducing global context information to capture global information and detailed information. Moreover, one compares the proposed method with FCN-8s, MSDNet, and U-Net network models on the large-scale multi-class UAV dataset UAVid. The experimental results indicate that the proposed method has higher performance in both MIoU and MPA, with an overall improvement of 9.2% and 8.5%, respectively, and its prediction capability is more balanced for both large-scale and small-scale targets.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of ICT Standardization
Journal of ICT Standardization Computer Science-Information Systems
CiteScore
2.20
自引率
0.00%
发文量
18
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信