SUN Xiaoye, MA Liyan, LI Gongyan, “Multi-vision Attention Networks for on-Line Red Jujube Grading,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1108-1117, 2019, doi: 10.1049/cje.2019.07.014
Citation: SUN Xiaoye, MA Liyan, LI Gongyan, “Multi-vision Attention Networks for on-Line Red Jujube Grading,” Chinese Journal of Electronics, vol. 28, no. 6, pp. 1108-1117, 2019, doi: 10.1049/cje.2019.07.014

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

doi: 10.1049/cje.2019.07.014
Funds:  This work is supported by National Key R&D Program of China (No.2018YFD0700300).
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  • Corresponding author: MA Liyan (corresponding author) was born in 1983.She received the Ph.D.degree in computer vision from Beijing Jiaotong University,Beijing,China,in 2013.From 2013 to 2018,she has been an assistant professor and associate professor with Institute of Microelectronics of Chinese Academy of Sciences,respectively.She is currently an associate professor with School of Computer Engineering and Sciences,Shanghai University,Shanghai,China.Her current research interests include computer vision,image processing,and deep learning.(Email:liyanma@shu.edu.cn)
  • Received Date: 2018-10-10
  • Rev Recd Date: 2019-05-16
  • Publish Date: 2019-11-10
  • 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.
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