Remote sensing small target detection based on feature multi-scale enhancement and interaction fusion

LI Yunhong; WEI Xiaoshuang; SU Xueping; LI Limin; TIAN Gufeng; HAO Teji; FENG Zhunruo; LI Shibo

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

您当前的位置：

首页 >

文章列表页 >

Remote sensing small target detection based on feature multi-scale enhancement and interaction fusion

更新时间：2025-04-21

- Remote sensing small target detection based on feature multi-scale enhancement and interaction fusion
- Journal of Northwest University (Natural Science Edition) Vol. 55, Issue 2, Pages: 277-285(2025)
- 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：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2025-02-005
  CLC： TP391.41
- Received：05 October 2024，
  
  Published：25 April 2025
- 稿件说明：
移动端阅览
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：

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

references

GUO K . RDSSD: Refinement deconvolutional single shot detector [J ] . Journal of Physics: Conference Series , 2020 , 1651 ( 1 ): 012140 .

QU J S , SU C , ZHANG Z W , et al . Dilated convolution and feature fusion SSD network for small object detection in remote sensing images [J ] . IEEE Access , 2020 , 8 : 82832 - 82843 .

WANG J L , CUI Z Y , JIANG T , et al . Lightweight deep neural networks for ship target detection in SAR imagery [J ] . IEEE Transactions on Image Processing , 2022 , 32 : 565 - 579 .

WU S X , WANG X H , GUO C C . Application of feature pyramid network and feature fusion single shot multibox detector for real-time prostate capsule detection [J ] . Electronics , 2023 , 12 ( 4 ): 1060 .

HAO Z , HONG X G , ZHU L . Detecting small objects in thermal images using single-shot detector [J ] . Automatic Control and Computer Sciences , 2021 , 55 ( 2 ): 202 - 211 .

DENG C F , WANG M M , LIU L , et al . Extended feature pyramid network for small object detection [J ] . IEEE Transactions on Multimedia , 2021 , 24 : 1968 - 1979 .

YIN Q J , YANG W Z , RAN M Y , et al . FD-SSD: An improved SSD object detection algorithm based on feature fusion and dilated convolution [J ] . Signal Processing: Image Communication , 2021 , 98 : 116402 .

徐丹青 , 吴一全 . 光学遥感图像目标检测的深度学习算法研究进展 [J ] . 遥感学报 , 2024 , 28 ( 12 ): 3045 - 3073 .

XU D Q , WU Y Q . Research progress on deep learning algorithms for object detection in optical remote sensing images [J ] . Journal of Remote Sensing , 2024 , 28 ( 12 ): 3045 - 3073 .

PANG J M , LI C , SHI J P , et al . R2-CNN: Fast tiny object detection in large-scale remote sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 8 ): 5512 - 5524 .

ZHANG G J , LU S J , ZHANG W . CAD-Net: A context-aware detection network for objects in remote sensing imagery [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 12 ): 10015 - 10024 .

李云红 , 刘杏瑞 , 谢蓉蓉 , 等 . 基于超分辨重建和公共特征子空间的低分辨率人脸识别 [J ] . 西北大学学报(自然科学版) , 2023 , 53 ( 2 ): 241 - 247 .

LI Y H , LIU X R , XIE R R , et al . Low resolution facial recognition based on super-resolution reconstruction and common feature subspace [J ] . Journal of Northwest University (Natural Science Edition) , 2023 , 53 ( 2 ): 241 - 247 .

ZHANG Y L , YUAN Y , FENG Y C , et al . Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 8 ): 5535 - 5548 .

李婕 , 周顺 , 朱鑫潮 , 等 . 结合多通道注意力的遥感图像飞机目标检测 [J ] . 计算机工程与应用 , 2022 , 58 ( 1 ): 209 - 217 .

LI J , ZHOU S , ZHU X C , et al . Aircraft target detection in remote sensing images using multi-channel attention [J ] . Computer Engineering and Applications , 2022 , 58 ( 1 ): 209 - 217 .

李云红 , 聂梦瑄 , 苏雪平 , 等 . 分区域特征提取的人脸识别算法 [J ] . 西北大学学报(自然科学版) , 2020 , 50 ( 5 ): 811 - 818 .

LI Y H , NIE M X , SU X P , et al . Facial recognition algorithm based on regional feature extraction [J ] . Journal of Northwest University (Natural Science Edition) , 2020 , 50 ( 5 ): 811 - 818 .

GONG Y Q , YU X H , DING Y , et al . Effective fusion factor in FPN for tiny object detection [C ] // 2021 IEEE Winter Conference on Applications of Computer Vision . Waikoloa : IEEE , 2021 : 1159 - 1167 .

CHENG G , SI Y J , HONG H L , et al . Cross-scale feature fusion for object detection in optical remote sensing images [J ] . IEEE Geoscience and Remote Sensing Letters , 2021 , 18 ( 3 ): 431 - 435 .

YANG X , YANG J R , YAN J C , et al . SCRDet: Towards more robust detection for small, cluttered and rotated objects [C ] // 2019 IEEE/CVF International Conference on Computer Vision . Seoul : IEEE , 2019 : 8231 - 8240 .

BOCHKOVSKIY A , WANG C Y , LIAO H M . YOLOv4: Optimal speed and accuracy of object detection [EB/OL ] . ( 2020-04-23 )[ 2024-09-25 ] . https://arxiv.org/abs/2004.10934v1 https://arxiv.org/abs/2004.10934v1 .

LIU W , ANGUELOV D , ERHAN D , et al . SSD: Single shot MultiBox detector [M ] //Computer Vision-ECCV 2016 . Cham : Springer International Publishing , 2016 : 21 - 37 .

LAW H , DENG J . CornerNnet: Detecting objects as paired keypoints [J ] . Computer Vision , 2020 , 128 ( 3 ): 642 - 656 .

HUANG Z C , LI W , XIA X G , et al . A novel nonlocal-aware pyramid and multiscale multitask refinement detector for object detection in remote sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2021 , 60 : 5601920 .

余俊宇 , 刘孙俊 , 许桃 . 融合注意力机制的YOLOv7遥感小目标检测算法研究 [J ] . 计算机工程与应用 , 2023 , 59 ( 20 ): 167 - 175 .

YU J Y , LIU S J , XU T . Research on YOLOv7 remote sensing small target detection algorithm incorporating attention mechanism [J ] . Computer Engineering and Applications , 2023 , 59 ( 20 ): 167 - 175 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰