Application of improved VGG-16 convolutional neural network algorithm on nitrile rubber sheet recognition

Abstract

In order to solve the problems of over fitting, large amount of parameters and low accuracy of VGG-16 convolutional neural network in nitrile rubber sheet recognition, an improved VGG-16 convolutional neural network recognition algorithm based on reducing the depth of the original network was proposed. The network structure was optimized by embedding multi-resolution packet convolution, replacing maximum pooling with mixed pooling, and adding switched normalization. The experimental results show that there is no over fitting in the training process, and the parameters are approximately reduced to 0.098% of VGG-16. Compared with VGG-16 network which only reduced the depth, the recognition accuracy was improved by 7.17%, which could be applied to the material recognition of some solid rocket motors with inner insulation.

Keywords

• nitrile rubber sheet
• convolutional neural network
• vgg-16
• the normalized
• mixed pooling
• multi-resolution grouped convolution