Image classification method based on local receptive field extended S-Mobilenet model
Hongmei Liu, Chuanwu Tan · Highlights in Science Engineering and Technology · 2022
Aiming at the problem that the lightweight deep neural network mobilenet will reduce the classification accuracy, the hollow convolution kernel is introduced into a convolution layer of mobilenet model, and a S-Mobilenet model based on local receptive field expansion is proposed. The model is divided into three structures according to the location of the hole convolution kernel. Without increasing the number of parameters, it can expand the local receptive field of the convolution kernel and improve the classification accuracy. The experiment was carried out on caltech-101 data set, caltech-256 data set and animal classification database of Tubingen University. The results show that S-Mobilenet model can obtain better classification accuracy than Mobilenet, which can be improved by 2% at most.