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基于分层卷积深度学习系统的植物叶片识别研究

张帅 淮永建

张帅, 淮永建. 基于分层卷积深度学习系统的植物叶片识别研究[J]. 北京林业大学学报, 2016, 38(9): 108-115. doi: 10.13332/j.1000-1522.20160035
引用本文: 张帅, 淮永建. 基于分层卷积深度学习系统的植物叶片识别研究[J]. 北京林业大学学报, 2016, 38(9): 108-115. doi: 10.13332/j.1000-1522.20160035
ZHANG Shuai, HUAI Yong-jian.. Leaf image recognition based on layered convolutions neural network deep learning.[J]. Journal of Beijing Forestry University, 2016, 38(9): 108-115. doi: 10.13332/j.1000-1522.20160035
Citation: ZHANG Shuai, HUAI Yong-jian.. Leaf image recognition based on layered convolutions neural network deep learning.[J]. Journal of Beijing Forestry University, 2016, 38(9): 108-115. doi: 10.13332/j.1000-1522.20160035

基于分层卷积深度学习系统的植物叶片识别研究

doi: 10.13332/j.1000-1522.20160035
基金项目: 

森林景观及林业生产过程仿真关键技术研究(2015ZCQ-XX)。

详细信息
    作者简介:

    张帅。主要研究方向:图像处理。Email:piaofeitest@qq.com地址:100083北京市海淀区清华东路35号北京林业大学信息学院。责任作者:淮永建,教授,博士生导师。主要研究方向:虚拟植物。Email:huaiyj@bjfu.edu.cn地址:同上。

    张帅。主要研究方向:图像处理。Email:piaofeitest@qq.com地址:100083北京市海淀区清华东路35号北京林业大学信息学院。责任作者:淮永建,教授,博士生导师。主要研究方向:虚拟植物。Email:huaiyj@bjfu.edu.cn地址:同上。

Leaf image recognition based on layered convolutions neural network deep learning.

  • 摘要: 深度学习已成为图像识别领域的研究热点。本文以植物叶片图像识别为研究对象,对单一背景和复杂背景图像分别给出了优化预处理方案;设计了一个8层卷积神经网络深度学习系统分别对Pl@antNet叶片库和自扩展的叶片图库中33 293张简单背景和复杂背景叶片图像进行训练和识别,并与传统基于植物叶片多特征的识别方法进行了比较分析。实验证明:本文提供的CNN+SVM和CNN+Softmax分类器识别方法对单一背景叶片图像识别率高达91.11%和90.90%,识别复杂背景叶片图像的识别率也能高达34.38%,取得了较好的识别效果。利用本文实现的分层卷积深度学习识别系统在数据量大而无法做出更多优化的情况下,叶片图像的识别率更高,尤其是针对复杂背景下的叶片图像,取得了极佳的识别效果。

     

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出版历程
  • 收稿日期:  2016-01-21
  • 刊出日期:  2016-09-30

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