A deep-learning-based method for classifying rice processing freshness
To improve the accuracy and speed of rice processing freshness classification,a classification method based on deep learning was proposed in this paper.Based on VGG-19 architecture,the method introduced SE(squeeze-and-excitation)attention mechanism to follow more closely the features of critical channels and substituted ReLU function with PReLU function for acti-vation purpose.Meanwhile,VGG-19 network was materially modified by replacing its bottom pooling layer with global mixed pooling and deleting the first two fully connected layers.Then,with rice freshness as the research object,the modified VGG-19 network was implemented to classify the rice by its freshness and was proven effective.Simulation results indicate the modified VGG-19 could accurately and quickly classify the rice by freshness.Its average accuracy,precision,recall ratio,and F1 value were 97.81％,97.63％,97.89％,and 97.56％,respectively.It was testified as fast in rice detection,as the test took only 275 s.The method proposed hereby did improve both the accuracy and speed of freshness-based rice processing classification.
万方数据
维普
