基于EIMYOLO的高分遥感图像目标检测

曹峰; 曾科文; 李德玉; 罗喜召; 陶重犇

doi:10.12263/DZXB.20250216

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基于EIMYOLO的高分遥感图像目标检测

学术论文 | 更新时间：2025-12-10

- 基于EIMYOLO的高分遥感图像目标检测
- Object Detection Based on EIMYOLO for High-Resolution Remote Sensing Images
- 电子学报 2025年53卷第7期页码：2266-2278
- 作者机构：
  
  1.山西大学计算机与信息技术学院，山西太原 030006
  2.苏州大学计算机科学与技术学院，江苏苏州 215006
  3.苏州科技大学电子与信息工程学院，江苏苏州 215009
- 作者简介：
  
  [ "曹峰男，1980年出生于山西省朔州市.现为山西大学计算机与信息技术学院教授、博士生导师.主要研究方向为人工智能、数据挖掘与模式识别、遥感信息处理.E-mail: caof@sxu.edu.cn" ]
  [ "曾科文男，2001年出生于四川省乐山市.现为山西大学计算机与信息技术学院硕士研究生.主要研究方向为高分遥感图像目标检测.E-mail: wenClark666@163.com" ]
  [ "李德玉男，1965年出生于山西省临汾市.现为山西大学计算机与信息技术学院教授、博士生导师.主要研究方向为数据挖掘、计算智能、粒计算.E-mail: lidy@sxu.edu.cn" ]
  [ "罗喜召男，1978年出生于河南省漯河市.现为苏州大学计算机科学与技术学院教授、博士生导师.主要研究方向为网络空间安全、人工智能安全.E-mail: xzluo@suda.edu.cn" ]
  [ "陶重犇男，1985年出生于江苏省昆山市.现为苏州科技大学电子与信息工程学院副教授、硕士生导师.主要研究方向为自动驾驶态势感知、车联网安全、双足机器人运动控制、无人机目标检测.E-mail: chongbentao@usts.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62472269;62072291;62473241;62436004;62372317;62472300)
- DOI：10.12263/DZXB.20250216
  中图分类号： TP391.4;
- 收稿：2025-03-23，
  
  修回：2025-05-22，
  
  纸质出版：2025-07-25
- 稿件说明：
移动端阅览
曹峰, 曾科文, 李德玉, 等. 基于EIMYOLO的高分遥感图像目标检测[J]. 电子学报, 2025, 53(07): 2266-2278.

CAO Feng, ZENG Ke-wen, LI De-yu, et al. Object Detection Based on EIMYOLO for High-Resolution Remote Sensing Images[J]. Acta Electronica Sinica, 2025, 53(07): 2266-2278.
曹峰, 曾科文, 李德玉, 等. 基于EIMYOLO的高分遥感图像目标检测[J]. 电子学报, 2025, 53(07): 2266-2278. DOI：10.12263/DZXB.20250216

CAO Feng, ZENG Ke-wen, LI De-yu, et al. Object Detection Based on EIMYOLO for High-Resolution Remote Sensing Images[J]. Acta Electronica Sinica, 2025, 53(07): 2266-2278. DOI：10.12263/DZXB.20250216

摘要

高分遥感图像目标检测是遥感信息智能化处理的研究热点，具有广泛的应用背景和重要的应用价值.相比于自然图像，高分遥感图像目标检测面临目标朝向任意、尺度变化大、背景复杂易受干扰以及排列密集度高等诸多难点.为了进一步提升高分遥感图像目标检测算法的性能，本文从特征融合与特征增强的角度出发，以YOLO11为基准算法提出一种旋转框遥感图像目标检测算法EIMYOLO，并设计了边缘特征增强、多尺度特征增强提取器和多尺度注意力机制动态融合3个即插即用的模块.边缘特征增强模块通过提取目标的边缘特征，提高了算法对旋转目标的方向敏感度以及复杂背景下的特征提取能力.多尺度特征增强提取器和多尺度注意力机制动态融合模块，分别从层内特征增强及层间特征融合角度出发，提高了算法对密集目标和细长目标的检测能力.为了验证本文算法的性能，在公共遥感数据集HRSC2016和DIOR-R上进行了实验.结果表明，所提算法的平均检测准精度分别达到了90.80%和72.40%，优于基准算法和对比算法.

Abstract

Object detection for high-resolution remote sensing images has become a key research area in intelligent remote sensing information processing

with extensive application scenarios and significant practical value. Unlike natural images

remote sensing images present unique challenges such as arbitrary object orientations

multi-scale variations

complex backgrounds

and densely arranged targets. To further improve the performance of high-resolution remote sensing image object detection

this paper proposes EIMYOLO

a novel rotated object detection algorithm based on YOLOv11

which incorporates innovative feature fusion and enhancement strategies. The proposed method integrates three plug-and-play modules designed to enhance feature representation and fusion. First

the Edge Feature Reinforcement Block improves orientation sensitivity and feature discriminability by extracting fine-grained edge information

especially in complex scenes. Second

the Interlayer Feature Enhancement Extractor boosts intralayer feature representation

particularly benefiting the detection of dense and elongated objects. Third

the Multi-Scale Attention Dynamic Fusion enhances inter-layer feature integration through adaptive attention mechanisms. Extensive experiments conducted on two benchmark datasets

HRSC2016 and DIOR-R

demonstrate the effectiveness of our approach

achieving state-of-the-art mean Average Precision (mAP) scores of 90.80% and 72.40%

respectively. These results confirm the superior performance of EIMYOLO over existing baseline and comparative methods.

关键词

Keywords

references

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