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计算机系统应用:2018,27(11):136-141
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基于全卷积神经网络的空间植物图像快速识别
樊帅1,2, 王鑫1,2, 阎镇1
(1.中国科学院 空间应用工程与技术中心 中国科学院太空应用重点实验室, 北京 100094;2.中国科学院大学, 北京 100049)
Fast Recognition of Space Plants Image Based on Fully Convolutional Networks
FAN Shuai1,2, WANG Xin1,2, YAN Zhen1
(1.CAS Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China;2.University of Chinese Academy of Sciences, Beijing 100049, China)
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投稿时间:2018-03-28    修订日期:2018-04-24
中文摘要: 为解决空间站内航天员长期生存自给自足的问题,空间植物的研究变得越来越重要.目前,图像识别领域存在着浅层图像识别方法难以提取空间植物图像分层特征,以及深层卷积神经网络方法固定尺寸输入和识别时间较长的问题.因此,本文提出的基于全卷积神经网络的方法,通过提取由浅层至深层的特征、深度融合光谱特征和空间特征,实现对空间植物图像的有效准确表示,从而实现空间植物图像的快速、精确识别.
Abstract:In order to solve the problem of the long-term survival of the astronauts in the space station, the research of space plants becomes more and more important. At present, there are some problems in image recognition field, such as the method of the shallow images recognition is difficult to extract hierarchical features of space plant images, and deep convolution neural network has fixed size input and long recognition time. To deal with these problems, a method based on fully convolutional networks is proposed in this study, and the networks have the ability to extract features from the shallow to deep, deep fusion spectrum features, and spatial features to achieve an efficient and accurate representation of the space plants image, so as to achieve fast and accurate recognition of the space plants image.
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樊帅,王鑫,阎镇.基于全卷积神经网络的空间植物图像快速识别.计算机系统应用,2018,27(11):136-141
FAN Shuai,WANG Xin,YAN Zhen.Fast Recognition of Space Plants Image Based on Fully Convolutional Networks.COMPUTER SYSTEMS APPLICATIONS,2018,27(11):136-141

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