[1]李林泽,张涛.基于深度学习的空间非合作目标特征检测与识别[J].智能系统学报,2020,15(6):1154-1162.[doi:10.11992/tis.202006011]
LI Linze,ZHANG Tao.Feature detection and recognition of spatial noncooperative objects based on deep learning[J].CAAI Transactions on Intelligent Systems,2020,15(6):1154-1162.[doi:10.11992/tis.202006011]
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LI Linze,ZHANG Tao.Feature detection and recognition of spatial noncooperative objects based on deep learning[J].CAAI Transactions on Intelligent Systems,2020,15(6):1154-1162.[doi:10.11992/tis.202006011]
基于深度学习的空间非合作目标特征检测与识别
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第6期
页码:
1154-1162
栏目:
学术论文—机器感知与模式识别
出版日期:
2020-11-05
- Title:
- Feature detection and recognition of spatial noncooperative objects based on deep learning
- 关键词:
- 空间非合作目标; 特征检测与识别; 深度学习; 区域全卷积网络; 头部轻量化卷积神经网络; 改进的Mask R-CNN; 数据集构建; 迁移学习
- Keywords:
- spatial noncooperative targets; feature detection and recognition; deep learning; R-FCN; light-head R-CNN; modified mask R-CNN; dataset construction; transfer learning
- 分类号:
- TP18;V19
- 摘要:
- 针对空间非合作目标检测与识别任务的智能化要求，本文将深度学习方法Mask R-CNN(mask region-based convolutional neural network)应用于任务中，并借鉴R-FCN(region-based fully convolutional networks)和Light-head R-CNN (light-head region-based convolutional neural network)对其进行优化改进，提升检测速度，以满足空间任务实时性要求。实验结果表明，与传统的Mask R-CNN相比，改进的Mask R-CNN可缩短20%的检测时间。针对深度神经网络需要大样本数据集进行训练的特点，本文基于迁移学习提出搭建虚拟环境进行样本采集，构造空间目标特征检测与识别数据集的方法。实验结果表明，网络在虚拟环境生成的数据集上可以很好地学习到相应特征，从而具备迁移到实际任务的能力。
- Abstract:
- To meet the intelligence requirements of feature detection and recognition of a spatial noncooperative target, the deep learning method—Mask Region-based Convolutional Network (Mask R-CNN)—is applied to the task using the idea of Region-based Fully Convolutional Network (R-FCN) and Light-head R-CNN as references to improve the detection speed to meet the real-time requirements of spatial tasks. Results obtained from the aforementioned study shows that the modified Mask R-CNN can shorten the detection time by 20% compared with the original version. Considering that deep neural networks require large sample datasets for training, this paper proposes a method for constructing a virtual environment for sample collection to build datasets, thus constructing the feature detection of spatial objects and datasets for recognition. The experimental result shows that the network learns the corresponding characteristics very well on the database generated from the virtual environment, and thus, the network can be migrated to the real task.
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备注/Memo
收稿日期:2020-06-09。
作者简介:李林泽，硕士研究生，主要研究方向为深度学习;张涛，教授，博士生导师，担任IEEE Education Society Beijing Chapter主席、中国自动化学会理事，主要研究方向为模式识别、非线性系统控制、机器人学、控制工程和人工智能。发表学术论文200余篇，出版专著8部
通讯作者:张涛.E-mail:taozhang@mail.tsinghua.edu.cn
更新日期/Last Update:
2020-12-25