摘要
在复杂背景下的光学遥感图像目标检测中,为了提高检测精度,同时降低检测网络复杂度,提出了面向光学遥感目标的全局上下文检测模型。首先,采用结构简单的特征编码-特征解码网络进行特征提取。其次,为提高对多尺度目标的定位能力,采取全局上下文特征与目标中心点局部特征相结合的方式生成高分辨率热点图,并利用全局特征实现目标的预分类。最后,提出不同尺度的定位损失函数,用于增强模型的回归能力。实验结果表明:当使用主干网络Root-Res-Net18时,本文模型在公开遥感数据集NWPU VHR-10上的检测精度可达97.6%AP50和83.4%AP75,检测速度达16 PFS,基本满足设计需求,实现了网络速度和精度的有效平衡,便于后续算法在移动设备端的移植和应用。
To improve the detection accuracy and reduce the complexity of optical remote sensing of target images with a complex background,a global context detection model based on optical remote sensing of targets is proposed.First,a feature encoder-feature decoder network is used for feature extraction.Then,to improve the positioning ability of multi-scale targets,a method that combines global-contextual features and target center local features is used to generate high-resolution heat maps.The global features are used to achieve the pre-classification of targets.Finally,a positioning loss function at different scales is proposed to enhance the regression ability of the model.Experimental results show that the mean average precision of the proposed model reaches 97.6%AP50 and 83.4%AP75 on the NWPU VHR-10 public remote sensing data set,and the speed reaches 16 PFS.This design can achieve an effective balance between accuracy and speed.It facilitates subsequent porting and application of the algorithm on the mobile device side,which meets design requirements.
作者
张瑞琰
姜秀杰
安军社
崔天舒
ZHANG Rui-yan;JIANG Xiu-jie;AN Jun-she;CUI Tian-shu(Key Laboratory of Electronics and Information Technology for Space Systems,National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《中国光学》
EI
CAS
CSCD
北大核心
2020年第6期1302-1313,共12页
Chinese Optics
基金
中国科学院复杂航天系统电子信息技术重点实验室自主部署基金(No.Y42613A32S)。
关键词
计算机视觉
目标检测
遥感图像
特征编码-特征解码
全局上下文特征
computer vision
object detection
remote sensing image
feature encoder-feature decoder
global-contextual feature
