基于深度学习的非合作目标感知研究进展

近年来随着卷积神经网络的发展，基于深度学习的图像感知技术取得了巨大进展。深度学习算法不依赖于人工辅助设计标记、泛化能力强、检测精度高，在空间非合作目标智能感知领域引起了国内外学者的关注。本文分析了应用深度学习方法进行非合作目标智能感知的研究现状，并对他们进行分类介绍与总结。首先，总结了空间非合作目标感知的在轨应用情况和任务阶段规划，接着分析了非合作目标的结构特性和表面光照特性；其次梳理总结了建立非合作目标数据集的三种方法，分类归纳了非合作目标识别与非合作目标位姿检测的国内外研究进展；最后，分析了基于深度学习的非合作目标智能感知方法的关键问题与难点，并给出了后续研究的思路。

Research Progress of Non-Cooperative Target Intelligent Perception Based on Deep Learning

In recent years, with the development of convolutional neural network, image sensing technology based on deep learning has made great progress. Because the deep learning algorithm does not rely on artificial aided design markers, has strong generalization ability and high detection accuracy, it has drawn attention to scholars at home and abroad in the field of intelligent perception of spatial non cooperative targets. This paper investigates the research status of intelligent perception of non-cooperative targets using deep learning methods, and introduces and summarizes them. Firstly, the on-orbit application and mission phase planning of space non-cooperative target perception are summarized, and then the structural characteristics and surface illumination characteristics of non-cooperative targets are analyzed; Secondly, three methods of establishing non cooperative target data sets are summarized, and the research status of non-cooperative target recognition and non-cooperative target pose detection at home and abroad is classified and summarized; Finally, the key problems and difficulties of non-cooperative target intelligent perception method based on deep learning are analyzed, and the ideas of follow-up research are given.