多尺度特征增强与交互融合的遥感小目标检测

李云红; 魏小双; 苏雪平; 李丽敏; 田谷丰; 郝特吉; 冯准若; 李仕博

doi:10.16152/j.cnki.xdxbzr.2025-02-005

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多尺度特征增强与交互融合的遥感小目标检测

人工智能图像处理 | 更新时间：2025-04-21

- 多尺度特征增强与交互融合的遥感小目标检测
- Remote sensing small target detection based on feature multi-scale enhancement and interaction fusion
- 西北大学学报（自然科学版） 2025年55卷第2期页码：277-285
- 作者机构：
  
  西安工程大学　电子信息学院，陕西　西安　 710048
- 作者简介：
  
  李云红，女，博士，教授，从事人工智能图像处理等研究，hitliyunhong@163.com。
- 基金信息：
  
  国家自然科学基金(62203344);陕西省科技厅自然科学基础研究重点项目(2022JZ-35);陕西高校青年创新团队
- DOI：10.16152/j.cnki.xdxbzr.2025-02-005
  中图分类号： TP391.41
- 收稿日期：2024-10-05，
  
  纸质出版日期：2025-04-25
- 稿件说明：
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李云红, 魏小双, 苏雪平, 等. 多尺度特征增强与交互融合的遥感小目标检测[J]. 西北大学学报（自然科学版）, 2025,55(2):277-285.

LI Yunhong, WEI Xiaoshuang, SU Xueping, et al. Remote sensing small target detection based on feature multi-scale enhancement and interaction fusion[J]. Journal of northwest university (natural science edition), 2025, 55(2): 277-285.
李云红, 魏小双, 苏雪平, 等. 多尺度特征增强与交互融合的遥感小目标检测[J]. 西北大学学报（自然科学版）, 2025,55(2):277-285. DOI： 10.16152/j.cnki.xdxbzr.2025-02-005.

LI Yunhong, WEI Xiaoshuang, SU Xueping, et al. Remote sensing small target detection based on feature multi-scale enhancement and interaction fusion[J]. Journal of northwest university (natural science edition), 2025, 55(2): 277-285. DOI： 10.16152/j.cnki.xdxbzr.2025-02-005.

摘要

针对遥感图像小目标检测任务中，存在目标细节纹理信息模糊导致特征提取与融合不佳、小目标漏检等问题，提出了一种基于多尺度特征增强与交互融合的遥感小目标检测算法。首先，采用跨层多分支连接结构的多尺度特征增强（multiscale feature enhancement，MFE）模块，利用Split分流操作丰富和增强不同梯度获取的纹理特征信息，同时引入轻量级特征幻影模块Ghost进行通道线性变换，生成更多有效的特征细节信息流，以增强对图像中局部细节特征信息的关注；其次，构建特征交互融合（feature interaction fusion，FIF）模块，引入多分支串并行的卷积块与自适应机制的池化块，交互输入特征的通道语义信息和空间特征变换，捕获全局上下文信息，精确小目标的关键位置信息，加强特征信息之间的相关性，实现细粒度特征的多维度交互融合。使用公开的光学遥感数据集DIOR验证所提算法，改进后的网络模型平均精度值为87.6%，与NPMMR-Det、YOLOv7、YOLOv5等其他7种优秀算法相比均有提高，改进后的遥感图像小目标检测算法取得了更好的检测精度。

Abstract

In order to achieve remote sensing image small object detection task

there are problems such as poor feature extraction and fusion due to the blurring of target detail texture information

and leakage of small targets

a remote sensing objects detection algorithm that leverages multiscale feature enhancement and interactive fusion is proposed. Firstly

the multiscale feature enhancement (MFE) module with cross-layer and multi-branch connection structure is used to enrich and enhance the texture feature information obtained from different gradients by using the Split shunt operation

and at the same time

the lightweight feature phantom module Ghost is introduced to perform the linear transformation of the channel

generating more effective feature detail information flow to enhance attention to local detail feature information in the image. Secondly

the feature interaction fusion (FIF) module is constructed

which introduces a multi-branch serial parallel convolution block and an adaptive mechanism pooling block to interact with the channel semantic information and spatial transformation of the input features

capture the global context information

and accurately locate the key position of the small targets. information

enhance the correlation between feature information

and achieve multi-dimensional interactive fusion of fine-grained features. The proposed algorithm is validated with DIOR

which is a remote sensing dataset. The optimized network model achieves a mean accuracy precision of 87.6%

which is higher than the other seven excellent algorithms including NPMMR-Det

YOLOv7

and YOLOv5. The improved small target detection algorithm for remote sensing images achieves better detection accuracy.

关键词

Keywords

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