首页|Multi-vision Attention Networks for on-Line Red Jujube Grading
Multi-vision Attention Networks for on-Line Red Jujube Grading
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原文链接
NETL
NSTL
万方数据
维普
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.
NETL
NSTL
万方数据
维普
