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Research of image recognition method based on enhanced inception-ResNet-V2

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Abstract

In order to improve the accuracy of CNN (convolutional neural network) in image classification, an enhanced Inception-ResNet-v2 model based on CNN is designed through the comparative study and analysis of the structure of classification model. This paper proposes to use multi-scale depthwise separable convolution to replace the convolution structure in Inception-ResNet-v2 model, which can reduce the amount of model parameters and extract features under different receptive fields. At the same time, this paper establishes channel filtering module based on global information comparison to filter and join channels, which realizes the effective extraction of features. Finally, through data enhancement, batch normalization and learning rate adjustment, the effect of the model used in this paper is better than most other models in each dataset, and the accuracy rate can reach 94.8%.

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Acknowledgements

This work is supported by Natural Science Foundation of China (No. 61871432, No. 61771492), the Natural Science Foundation of Hunan Province (No.2020JJ4275, No.2019JJ6008, and No.2019JJ60054), National College Students’ research based learning and innovation experimental project(No.201811535012), and Research based learning and innovative experiment project for college students in Hunan Province(No.S201911535027).

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Authors and Affiliations

  1. School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China

    Cheng Peng, Yikun Liu, Xinpan Yuan & Qing Chen

  2. School of Automation, Central South University, Changsha, China

    Cheng Peng

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  1. Cheng Peng
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  2. Yikun Liu
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  3. Xinpan Yuan
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  4. Qing Chen
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Correspondence to Xinpan Yuan.

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Peng, C., Liu, Y., Yuan, X. et al. Research of image recognition method based on enhanced inception-ResNet-V2. Multimed Tools Appl 81, 34345–34365 (2022). https://doi.org/10.1007/s11042-022-12387-0

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  • DOI: https://doi.org/10.1007/s11042-022-12387-0

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