Classification of Trash Types in Cotton Based on Deep Learning

被引：0
|
作者：
Cai, Yueyue [1 ,2 ]
Wu, Jin [1 ,2 ]
Zhang, Chen [1 ]
机构：
[1] Wuhan Univ Sci & Technol, Wuhan 430081, Peoples R China
[2] Wuhan Univ Sci & Technol, Engn Res Ctr Met Automat & Measurement Technol, Minist Educ, Wuhan 430081, Hubei, Peoples R China
来源：
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC) | 2019年
关键词：
depth-wise separable convolutions; pointwise convolution; deep neural networks; classification; FOREIGN FIBERS;
D O I：
10.23919/chicc.2019.8865475
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A classification method of trash types in cotton using depth-wise separable convolutions to enrich features in deep neural networks is proposed. The method based on Vgg16 what is a typical neural network adopts depth-wise separable convolutions and deals with the output feature of every layer to rich feature much further. Then. the pointwise convolution is used to connect the features. Finally, the fully connected layer combines the whole features to classify trash types. The experiment results show that this method can improve the classification accuracy of trash types in cotton.
引用
收藏
页码：8783 / 8788
页数：6
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