NDLI: Multi-vision Attention Networks for on-Line Red Jujube Grading

Content Provider	IET Digital Library
Author	Sun, Xiaoye Ma, Liyan Li, Gongyan
Abstract	To solve the red jujube classification problem, this paper designs a convolutional neural network model with low computational cost and high classification accuracy. The architecture of the model is inspired by the multi-visual mechanism of the organism and DenseNet. To further improve our model, we add the attention mechanism of SE-Net. We also construct a dataset which contains 23,735 red jujube images captured by a jujube grading system. According to the appearance of the jujube and the characteristics of the grading system, the dataset is divided into four classes: invalid, rotten, wizened and normal. The numerical experiments show that the classification accuracy of our model reaches to 91.89%, which is comparable to DenseNet-121, InceptionV3, InceptionV4, and Inception-ResNet v2. Our model has real-time performance.
Starting Page	1108
Ending Page	1117
Page Count	10
ISSN	10224653
Volume Number	28
e-ISSN	20755597
Issue Number	Issue 6, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/28/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2019.07.014
Journal	Chinese Journal of Electronics
Publisher Date	2019-11-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Agricultural Products Agriculture Computational Complexity Computer Vision Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Model Fisheries Computing Forestry Image Denoising Learning in AI Low Computational Cost Multivision Attention Network Multivisual Mechanism Neural Computing Technique On-line Red Jujube Grading Optical, Image And Video Signal Processing Red Jujube Classification Problem SE-Net
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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