1（College of Education Science and Technology，Zhejiang University of Technology，Hangzhou 310023，China）2（College of Computer Science and Technology，Zhejiang University of Technology，Hangzhou 310023，China）3（Information Engineering，East China University of Technology，Nanchang 330013，China）
Abstract：The convolutional neural network has high computational complexity that takes up a lot of resources，making it difficult to deploy to mobile embedded devices.In order to solve this kind of limit，this paper puts forward a kind of based on Blueprint Separation Convolution is Embedded in Parallel Channels（B2SENet）for Image Classification，Firstly by introducing parallel SENet channel，makes different parallel branches fuses in together，the size of the kernel enhanced global receptive field，Secondly，the traditional convolution operation mode is changed with parallel SE channels，adapter blueprint separation convolution（BS），effectively reduce convolution layer parameters of the model and volume.Finally introduced attention in the process of parallel channel integration mechanism，gain weight coefficients of the channel，strengthen the modeling on characteristics of selective，in 100 and ImageNet2012 CIFAR10 and benchmark data sets on the contrast，experiments show that BS2ENet（BS + Parallel channels SENet）on the model complexity and accuracy is superior to the existing most advanced models，its excellent performance in the embedded devices network model.
2021, Vol. 42 
