基于Inception V3的图像状态分类技术

王旖旎

doi:10.3788/YJYXS20203504.0389

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基于Inception V3的图像状态分类技术

图像处理 | 更新时间：2020-07-05

- 基于Inception V3的图像状态分类技术
- Image classification technology based on inception V3
- 液晶与显示 2020年35卷第4期页码：389-394
- 作者机构：
  
  重庆商务职业学院, 重庆 401331
- 作者简介：
  
  [ "王旖旎(1981-), 女, 重庆人, 硕士, 副教授, 2012年于重庆师范大学获得硕士学位, 主要从事智能计算、数据挖掘、软件技术等方面的研究。E-mail: wangyini0917@126.com" ]
- 基金信息：
  
  2019年重庆市教育委员会科学技术研究项目(KJQN201904402);重庆商务职业学院2018年度校级重点课题(2018XJKTZD06)
- DOI：10.3788/YJYXS20203504.0389
  中图分类号： TP391.4
- 收稿日期：2019-09-26，
  
  修回日期：2019-11-04，
  
  录用日期：2019-11-4，
  
  纸质出版日期：2020-04-05
- 稿件说明：
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王旖旎. 基于Inception V3的图像状态分类技术[J]. 液晶与显示, 2020,35(4):389-394.

ni WANG-Yi. Image classification technology based on inception V3[J]. Chinese journal of liquid crystals and displays, 2020, 35(4): 389-394.
王旖旎. 基于Inception V3的图像状态分类技术[J]. 液晶与显示, 2020,35(4):389-394. DOI： 10.3788/YJYXS20203504.0389.

ni WANG-Yi. Image classification technology based on inception V3[J]. Chinese journal of liquid crystals and displays, 2020, 35(4): 389-394. DOI： 10.3788/YJYXS20203504.0389.

摘要

为了实现对物体状态的分类识别，本文在GoogLeNet的Inception V3模块基础上进行了优化，使用Tanh作为激活函数并结合RMSprop，SGD优化器提升了模型的准确率。首先采用三次卷积插值，GAN对图像集进行预处理，再利用Inception对图像进行训练，最后结合RMSprop和SGD优化器对模型进行优化。用本文提出的模型在20个烹饪对象的图像上进行实验，结果表明，本文优化的Inception V3模型能够以71.5%的准确度对这些图像的状态进行分类，与对比算法相比，在分类准确度、训练损失上都有明显提升，可以满足图像分类的可靠性、稳定性等要求。

Abstract

In order to realize the classification and recognition of object state

the Inception V3 module on the basis of GoogLeNet is optimized Using Tanh as the activation function and RMSprop

the SGD optimizer improves the accuracy of the model. Firstly

using cubic convolution interpolation

GAN preprocesses the image set. Then

the Inception is used to train the image. Finally

the SGD optimizer is combined with RMSprop to optimize the model. Experiments on the images of 20 cooking objects using the model proposed in this paper show that the optimized Inception V3 model can classify the state of these images with 71.5% accuracy. Compared with the comparison algorithm

the classification accuracy and training loss are obviously improved

which can meet the requirements of reliability and stability of image classification.

关键词

Keywords

references

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