[1]赵文清,李溢晔.基于动态超图与多尺度特征融合的遥感图像目标检测[J].智能系统学报,2026,21(2):399-409.[doi:10.11992/tis.202508009]
ZHAO Wenqing,LI Yiye.Remote sensing image object detection based on dynamic hypergraphs and multi-scale feature fusion[J].CAAI Transactions on Intelligent Systems,2026,21(2):399-409.[doi:10.11992/tis.202508009]
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ZHAO Wenqing,LI Yiye.Remote sensing image object detection based on dynamic hypergraphs and multi-scale feature fusion[J].CAAI Transactions on Intelligent Systems,2026,21(2):399-409.[doi:10.11992/tis.202508009]
基于动态超图与多尺度特征融合的遥感图像目标检测
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第2期
页码:
399-409
栏目:
学术论文—机器学习
出版日期:
2026-03-05
- Title:
- Remote sensing image object detection based on dynamic hypergraphs and multi-scale feature fusion
- Keywords:
- remote sensing image; object detection; multi-scale; feature fusion; dynamic hypergraph; semantic features; coordinate attention; dilated convolution
- 分类号:
- TP391
- 摘要:
- 遥感图像目标尺度差异大、背景复杂，而目标检测模型存在多尺度感知能力不足、全局语义特征建模能力差的问题。本文提出了一种基于动态超图与多尺度特征融合的遥感图像目标检测模型。构造多尺度空洞卷积特征融合模块，并设计对应的特征提取网络，充分提取多尺度特征；构造动态门控超图模块，以此构建全局语义特征建模网络，强化对目标特征区域的感知，弱化复杂背景的干扰；提出多通道坐标注意力模块，结合坐标注意力机制与多尺度通道交互，增强特征的表达。在DIOR和RSOD两个数据集上设计了多组消融实验，结果表明，本模型在DIOR数据集与RSOD数据集上的平均精度均值比YOLO11模型分别提升2.5和2.3百分点，显著提升了遥感图像目标检测的精度。为验证本模型的有效性，设计对比实验与不同方法比较，实验结果证明本模型的检测效果优于其他方法。
- Abstract:
- Remote sensing images exhibit significant variations in target scales and complex backgrounds, while existing object detection models suffer from limited multi-scale perception and insufficient global semantic modeling capabilities. To address these challenges, a remote sensing object detection framework based on dynamic hypergraph and multi-scale feature fusion was proposed. First, a multi-scale dilated convolution feature fusion module was constructed, and a feature extraction network was designed to fully extract multi-scale features. Second, a dynamic gated hypergraph module was developed to establish a global semantic feature modeling network, which enhanced target feature perception while weakening complex background interference. Finally, a multi-channel coordinate attention module was presented, combining coordinate attention mechanisms with multi-scale channel interactions to strengthen feature representation. Ablation experiments are conducted on the DIOR and the RSOD datasets, demonstrating that the proposed model achieves 2.5 and 2.3 percent age point improvements in mean average precision over the YOLO11 baseline. Comparative experiments validate the superiority of the proposed model, showing enhanced detection performance against other methods.
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备注/Memo
收稿日期:2025-8-8。
基金项目:国家自然科学基金项目(62371188).
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。E-mail：zhaowenqing@ncepu.edu.cn。;李溢晔，硕士研究生，主要研究方向为深度学习与遥感图像处理。E-mail：liyiye2025@163.com。
通讯作者:赵文清. E-mail：zhaowenqing@ncepu.edu.cn
更新日期/Last Update:
1900-01-01