一种基于注意机制和卷积神经网络的视觉模型

A Visual Model Based on Attention Mechanism and Convolutional Neural Network

针对目前的深度卷积神经网络(CNN)模型规模大、训练参数多、计算速度慢以及难以移植到移动端等问题,提出了一种深度可分离卷积结合3重注意机制模块(DSC-TAM)的视觉模型。首先,通过深度可分离卷积网络来减少模型参数,提高网络模型的计算速度;其次,引入3重注意机制模块提高网络的特征提取能力,改善网络性能。实验结果表明:该方法的识别率可达99.63%,模型规模降低了13%;与标准卷积神经网络视觉模型及其他方法比较,在保证识别精度的同时减少了网络模型的大小。

To solve the problems of current deep convolutional neural network(CNN),such as large model size,many training parameters,slow computing speed,and difficulty in transplantation to mobile terminal,a visual model of depthwise separable convolution with triple attention module(DSC-TAM)is proposed.Firstly,the depthwise separable convolution is used to reduce the model parameters and improve the computing speed of the network model.Secondly,the triple attention mechanism module is introduced to improve the ability of feature extraction and network performance.The experimental results show that the recognition rate of this method is 99.63%,the model size is reduced by 13%.Compared with the standard convolutional neural network visual model and other methods,the recognition accuracy is guaranteed,and the size of the network model is reduced.