摘要
针对月球表面先验图像信息获取困难的问题,提出一种基于对抗神经网络的月表先验图像数据增强方法。在获取少量月表图像及障碍背景分割图的基础上,构建基于对抗神经网络的月表图像数据增强架构,利用新的障碍背景分割图匹配生成月表图像,扩充月表先验图像数据,可用于月球探测中障碍检测算法设计验证。仿真结果证明了所提方法生成的月表图像接近真实拍摄图像,且通过数据增强图像数据,使障碍检测结果获得明显提升,证明了方法的有效性。
In this paper,a method of lunar surface priori image data enhancement based on adversarial neural network is proposed to address the problem of difficulty in acquiring a priori image information on the lunar surface.Based on the acquisition of a small amount of lunar surface images and obstacle background segmentation maps,the lunar surface image data enhancement architecture based on the adversarial neural network is constructed,and the new obstacle background segmentation maps are used to match the lunar surface images and expand the lunar surface priori image data,which can be used for the design and verification of obstacle detection algorithms in lunar exploration.Simulation results prove that the lunar surface images generated by the proposed method are close to the real captured images,and the image data is enhanced by the data to obtain obvious improvement of the obstacle detection results,which proves the effectiveness of the proposed method.
作者
宋婷
兀泽朝
高艾
袁建平
SONG Ting;WU Zezhao;GAO Ai;YUAN Jianping(School of Astronautics,Northwestern Polytechnical University,Xi’an 710072,China;Shanghai Aerospace Control Technology Institute,Shanghai 201109,China;Institute of Mechanical and Electrical Engineering,Beijing 100074,China;Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory,Beijing 100074,China;School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2023年第10期3041-3048,共8页
Systems Engineering and Electronics
基金
国家自然科学基金(11872110)资助课题。
关键词
月球探测
数据增强
深度学习
对抗神经网络
lunar exploration
data enhancement
deep learning
adversarial neural networks
