Abstract：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.
李鹤喜,李记花,李威龙. 一种基于注意机制和卷积神经网络的视觉模型[J]. 计量学报, 2021, 42(7): 840-845.
LI He-xi,LI Ji-hua,LI Wei-long. A Visual Model Based on Attention Mechanism and Convolutional Neural Network. Acta Metrologica Sinica, 2021, 42(7): 840-845.
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2021, Vol. 42 
