YGNet: A Lightweight Object Detection Model for Remote Sensing

Xin Song;Erhao Gao
{"title":"YGNet: A Lightweight Object Detection Model for Remote Sensing","authors":"Xin Song;Erhao Gao","doi":"10.1109/LGRS.2024.3497575","DOIUrl":null,"url":null,"abstract":"In the dynamic field of remote sensing images (RSIs), the challenge of object scale variability and sensor resolution disparities is formidable. Addressing these complexities, we have designed a lightweight remote sensing model named YGNet, tailored for multiscale object detection. It demonstrates excellent performance in detecting both multiscale and small objects within RSIs. The E-RMSK module within YGNet employs a gradient-based architecture with multiple parallel reparameterized convolutions in its internal branches, facilitating the extraction of multiscale features while maintaining parameter and computational efficiency. The HLS-PAN structure integrates feature maps extracted through feature selection, enabling the top layers to relay image information downward to lower levels and the lowest layers to transmit data upward for localization, achieving feature fusion. This synergistic effect of the module design enhances the accuracy of object detection in complex remote sensing scenarios and ensures the model’s feasibility on platforms with limited resources. Rigorous testing on the RSOD and NWPU VHR-10 datasets has proven YGNet’s exceptional capabilities, achieving the mean average precision (mAP) scores of 96.2% and 88.9%, respectively. The model meets the demands for real-time, lightweight, multiscale object detection in remote sensing imagery, making it highly suitable for deployment in resource-constrained environments.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10752592/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In the dynamic field of remote sensing images (RSIs), the challenge of object scale variability and sensor resolution disparities is formidable. Addressing these complexities, we have designed a lightweight remote sensing model named YGNet, tailored for multiscale object detection. It demonstrates excellent performance in detecting both multiscale and small objects within RSIs. The E-RMSK module within YGNet employs a gradient-based architecture with multiple parallel reparameterized convolutions in its internal branches, facilitating the extraction of multiscale features while maintaining parameter and computational efficiency. The HLS-PAN structure integrates feature maps extracted through feature selection, enabling the top layers to relay image information downward to lower levels and the lowest layers to transmit data upward for localization, achieving feature fusion. This synergistic effect of the module design enhances the accuracy of object detection in complex remote sensing scenarios and ensures the model’s feasibility on platforms with limited resources. Rigorous testing on the RSOD and NWPU VHR-10 datasets has proven YGNet’s exceptional capabilities, achieving the mean average precision (mAP) scores of 96.2% and 88.9%, respectively. The model meets the demands for real-time, lightweight, multiscale object detection in remote sensing imagery, making it highly suitable for deployment in resource-constrained environments.
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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