Open AccessMulti‐vision Attention Networks for on‐Line Red Jujube Grading
Author(s) -
Sun Xiaoye,
Ma Liyan,
Li Gongyan
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.07.014
Subject(s) - computer science , convolutional neural network , artificial intelligence , grading (engineering) , pattern recognition (psychology) , residual neural network , civil engineering , engineering
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.