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一种基于CLMF的深度卷积神经网络模型

随婷婷 王晓峰

随婷婷, 王晓峰. 一种基于CLMF的深度卷积神经网络模型. 自动化学报, 2016, 42(6): 875-882. doi: 10.16383/j.aas.2016.c150741
引用本文: 随婷婷, 王晓峰. 一种基于CLMF的深度卷积神经网络模型. 自动化学报, 2016, 42(6): 875-882. doi: 10.16383/j.aas.2016.c150741
SUI Ting-Ting, WANG Xiao-Feng. Convolutional Neural Networks with Candidate Location and Multi-feature Fusion. ACTA AUTOMATICA SINICA, 2016, 42(6): 875-882. doi: 10.16383/j.aas.2016.c150741
Citation: SUI Ting-Ting, WANG Xiao-Feng. Convolutional Neural Networks with Candidate Location and Multi-feature Fusion. ACTA AUTOMATICA SINICA, 2016, 42(6): 875-882. doi: 10.16383/j.aas.2016.c150741

一种基于CLMF的深度卷积神经网络模型

doi: 10.16383/j.aas.2016.c150741
基金项目: 

国家自然科学基金 31170952

国家海洋局项目 201305026

上海海事大学优秀博士学位论文培育项目 2014bxlp005

上海海事大学研究生创新基金项目 2014ycx047

详细信息
    作者简介:

    王晓峰 上海海事大学教授, 博士. 主要研究方向为深度学习, 人工智能, 数据挖掘与知识发现

    通讯作者:

    随婷婷 上海海事大学信息工程学院博士研究生. 2013年获得上海海事大学信息工程学院硕士学位. 主要研究方向为深度学习, 人工智能, 数据挖掘与知识发现. 本文通信作者. E-mail: suisui61@163.com

  • 中图分类号: 

Convolutional Neural Networks with Candidate Location and Multi-feature Fusion

Funds: 

National Natural Science Foundation of China 31170952

Foundation of the National Bureau of Oceanogra- phy 201305026

Excellent Doctoral Dissertation Cultivation Foundation of Shanghai Maritime University 2014bxlp005

Graduate Innovation Foundation of Shanghai Maritime Univer- sity 2014ycx047

More Information
    Author Bio:

    WANG Xiao-Feng Ph. D., professor at Shanghai Maritime University. His research interest covers deep learning, articial intelligence, data mining and knowledge discovery

    Corresponding author: SUI Ting-Ting Ph. D. candidate at the College of Information Engineering, Shanghai Maritime University. She received her master degree from the College of Information Engineering, Shanghai Maritime University in 2013. Her research interest covers deep learning, articial intelligence, data mining and knowledge discovery. Corresponding author of this paper. E-mail:regnier@ibpc.fr
  • 摘要: 针对传统人工特征提取模型难以满足复杂场景下目标识别的需求, 提出了一种基于CLMF的深度卷积神经网络(Convolutional neural networks with candidate location and multi-feature fusion, CLMF-CNN).该模型结合视觉显著性、多特征融合和CNN模型实现目标对象的识别. 首先, 利用加权Itti模型获取目标候选区; 然后, 利用CNN模型从颜色、亮度多特征角度提取目标对象的特征, 经过加权融合供目标识别; 最后, 与单一特征以及目前的流行算法进行对比实验, 结果表明本文模型不仅在同等条件下正确识别率得到了提高, 同时, 达到实时性要求.
  • 图  1  深度卷积神经网络的结构图

    Fig.  1  The structure chart of CNN model

    图  2  CLMF-CNN模型结构图

    Fig.  2  The structure chart of CLMF-CNN model}

    图  3  目标候选区域提取效果图

    Fig.  3  The extraction of object candidate

    图  4  CNN模型添加候选目标后的识别效果对比图

    Fig.  4  The recognition performance of CNN model with candidate objects

    图  5  覆盖率对比图

    Fig.  5  The comparison chat of OV

    图  6  目标识别时耗对比图

    Fig.  6  The comparison chat of time consumption on object recognition

    图  7  CNN模型添加多特征后的识别效果对比图

    Fig.  7  The recognition performance of CNN model with multi-features

    图  8  不同方法的分类效果对比图

    Fig.  8  Recognition performance of different methods

    表  1  本文方法参数设置表

    Table  1  Parameters setting of our method

    层数种类特征图个数卷积核大小
    1卷积层1007£7
    2下采样层1002£2
    3卷积层1504£4
    4下采样层1502£2
    5卷积层2504£4
    6下采样层2502£2
    7全连接层3001£1
    8全连接层81£1
    激活函数Sigmoid
    损失函数Mean square error
    下载: 导出CSV

    表  2  CLMF-CNN模型的图像标注效果

    Table  2  The image annotation performance of CLMF-CNN

    标识图像标注信息
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-11-03
  • 录用日期:  2016-03-24
  • 刊出日期:  2016-06-20

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