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Multi-vision Attention Networks for On-line Red Jujube Grading by Sun, Xiaoye; Ma, Liyan; Li, Gongyan is a scholarly article available to read on EtoBox.

What is Multi-vision Attention Networks for On-line Red Jujube Grading about?

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. However, our model has real-time performance.

Author

Sun, Xiaoye; Ma, Liyan; Li, Gongyan

Published

2019

Language

EN