基于改进YOLOv3的高分辨率遥感图像复合目标检测  

Composite Object Detection Based on Improved YOLOv3 from High-resolution Remote Sensing Image

作　　者：张飙 王慧贤 韩冰 ZHANG Biao;WANG Hui-xian;HAN Bing(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100190,China;School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Spatial Information Processing and Application System Technology,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院空天信息创新研究院,北京100190 [2]中国科学院大学电子电气与通信工程学院,北京100049 [3]中国科学院空间信息处理与应用系统技术重点实验室,北京100190

出　　处：《计算机与现代化》2022年第12期74-80,共7页Computer and Modernization

基　　金：国家自然科学基金资助项目(62171436)。

摘　　要：遥感图像的复合目标相对单一目标而言,存在多个结构,结构之间存在一定差异。本文围绕复合目标多变性、复杂性,大宽幅遥感影像背景复杂,存在较多和待检复合目标特征相似的区域,检测准确率较低等问题,开展基于高分辨率遥感图像的复合目标检测研究。首先开展目标特性分析和样本数据标注;然后提出一种基于Coordinate Attention注意力机制和Focal Loss损失函数的改进YOLOv3检测算法;最后以篮球场这种复合目标为例进行实验。实验结果表明,改进后的算法与原YOLOv3算法相比,召回率和平均检测准确率分别提高了10.3个百分点和28.8个百分点。该结果验证了所提方案的可行性、合理性。Compared with a single object, the composite object of remote sensing image has multiple structures, and there are certain differences between the structures. The composite object has the characteristics of variability and complexity;the remote sensing image is wide and the background is complex, and there are many areas similar to the characteristics of the composite object to be inspected. The above two points lead to the low accuracy of the composite object detection. In response to this problem, this article develops research on composite object detection based on high-resolution remote sensing images. This paper first carries out object characteristic analysis and sample data labeling;then proposes an improved YOLOv3 detection algorithm based on Coordinate Attention attention mechanism and Focal Loss function;finally, an experiment is carried out with a composite target of a basketball court as an example. The experimental results show that compared with the original YOLOv3 algorithm, the recall rate and average detection accuracy of the improved algorithm are increased by 10.3 percentage points and 28.8 percentage points, respectively. The result verifies the feasibility and rationality of the proposed scheme.

关 键 词：目标检测 注意力机制 损失函数 复合目标 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]