[1]赵文清,康怿瑾,赵振兵,等.改进YOLOv5s的遥感图像目标检测[J].智能系统学报,2023,18(1):86-95.[doi:10.11992/tis.202203013]
ZHAO Wenqing,KANG Yijin,ZHAO Zhenbing,et al.A remote sensing image object detection algorithm with improved YOLOv5s[J].CAAI Transactions on Intelligent Systems,2023,18(1):86-95.[doi:10.11992/tis.202203013]
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ZHAO Wenqing,KANG Yijin,ZHAO Zhenbing,et al.A remote sensing image object detection algorithm with improved YOLOv5s[J].CAAI Transactions on Intelligent Systems,2023,18(1):86-95.[doi:10.11992/tis.202203013]
改进YOLOv5s的遥感图像目标检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
18
期数:
2023年第1期
页码:
86-95
栏目:
学术论文—智能系统
出版日期:
2023-01-05
- Title:
- A remote sensing image object detection algorithm with improved YOLOv5s
- Keywords:
- remote sensing images; objects of interest; object detection; feature extraction; efficient channel attention structure; multiscale feature fusion; contextual information; Swin Transformer; coordinate attention mechanism
- 分类号:
- TP751;TP391
- 摘要:
- 针对遥感图像中感兴趣目标特征不明显、背景信息复杂、小目标居多导致的目标检测精度较低的问题，本文提出了一种改进YOLOv5s的遥感图像目标检测算法（Swin-YOLOv5s）。首先，在骨干特征提取网络的卷积块中加入轻量级通道注意力结构，抑制无关信息的干扰；其次，在多尺度特征融合的基础上进行跨尺度连接和上下文信息加权操作来加强待检测目标的特征提取，将融合后的特征图组成新的特征金字塔；最后，在特征融合的过程中引入Swin Transformer网络结构和坐标注意力机制，进一步增强小目标的语义信息和全局感知能力。将本文提出的算法在DOTA数据集和RSOD数据集上进行消融实验,结果表明,本文提出的算法能够明显提高遥感图像目标检测的平均准确率。
- Abstract:
- Aiming at the low average target detection accuracy in remote sensing images caused by obscure features in the objects of interest, complex background information, and multiple small targets, we propose a new remote sensing image object detection algorithm with improved YOLOv5s (Swin-YOLOv5s). First, an efficient channel attention structure is added to the convolutional block of the backbone feature extraction network to suppress the interference of irrelevant information; second, cross-scale connection and contextual information weighting operations are performed to enhance detection target feature extraction on the basis of multiscale feature fusion, and the fused feature maps are composed into a new feature pyramid; finally, the Swin Transformer structure and coordinate attention mechanism are used to further enhance the semantic information and global perception ability of small targets. The result of a feature fusion elimination experiment performed on the DOTA and RSOD datasets shows that the proposed algorithm can significantly improve the average accuracy of object detection in remote sensing images.
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备注/Memo
收稿日期:2022-03-08。
基金项目:河北省自然科学基金项目(F2021502013);中央高校基本科研业务费面上项目(2020MS153,2021PT018);国家自然科学基金项目(61773160,61871182).
作者简介:赵文清,教授,博士,主要研究方向为人工智能与图像处理。获河北省科技进步二等奖、三等奖各1项。发表学术论文50余篇;康怿瑾,硕士研究生,主要研究方向为深度学习与目标检测;赵振兵,教授,博士,主要研究方向为电力视觉。主持国家自然科学基金等纵向课题10项。获省科技进步一等奖1项(第三完成人)。以第一完成人获得国家专利授权16项;以第一作者出版专著2部、发表学术论文50余篇
通讯作者:赵文清.E-mail:jbzwq@126.com
更新日期/Last Update:
1900-01-01