[1]GAO Shuping,ZHAO Qingyuan,QI Xiaogang,et al.Research on the improved image classification method of MobileNet[J].CAAI Transactions on Intelligent Systems,2021,16(1):11-20.[doi:10.11992/tis.202012034]
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Research on the improved image classification method of MobileNet

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 1 Page number: 11-20 Column: 学术论文—机器学习 Public date: 2021-01-05
Title:
Research on the improved image classification method of MobileNet
Author(s):
GAO Shuping;  ZHAO Qingyuan;  QI Xiaogang;  CHENG Mengfei
School of Mathematics and Statistics, Xidian University, Xi’an 710126, China
Keywords:
convolutional neural network; image classification; feature extraction; MobileNet; depth separable convolution; activation function; Leaky ReLU; residual structure
CLC:
TP391;TP181
DOI:
10.11992/tis.202012034
Abstract:
This paper proposes an improved strategy for the MobileNet neural network (L-MobileNet) because the feature extraction ability of a neural network structure is insufficient, and the classification accuracy is not high on the dataset containing complex image features. First, the original standard convolution form is replaced by the depth separable convolution form, and the feature map obtained from the deep convolution layer is reversed and transferred to the next layer through the deep convolution fusion layer. Second, the leaky ReLU activation function is used to replace the original ReLU activation function to retain more positive and negative feature information in the image, and residual structure is added to avoid the gradient diffusion phenomenon. Finally, the experimental results showed that when compared with six methods, L-MobileNet achieved the best results in the datasets of Cifar-10, Cifar-100 (coarse), Cifar-100 (fine), and Dogs vs Cats.
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