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Image recognition algorithm based on artificial intelligence

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Abstract

Convolutional neural networks also encountered some problems in the development of image recognition. The most prominent problem is that it is costly and time-consuming to collect data sets and train models. Limited data sets will cause the trained models to overfit. This paper proposes two methods to reduce overfitting based on the residual neural network architecture. The first type of method proposes a method of cross-combining waivers, reducing the size of the convolution kernel, and reducing the number of convolution kernels. The fitting method uses cross-combination to make the accuracy of Kaggle cat and dog data on the validation data set reach 95.37% and 90.31% on 30 types of engineering practice verification data set. The second method is based on the finetune residual neural network. A method of recurrent finetune residual neural network is proposed to improve the accuracy of the model. The accuracy of the finetune residual neural network on the Kaggle cat and dog validation dataset is 99.37%, and the accuracy of the dataset is verified in 30 types of engineering practice. The accuracy is 99.30%. The residual neural network method achieves 99.68% accuracy in the Kaggle cat and dog validation dataset and 99.61% in the validation dataset for 30 types of engineering practice.

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Acknowledgements

This work was financially supported by the National Natural Science Youth Foundation of China (Grant Number 61805067).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, 300130, China

    Hong Chen, Hongdong Zhao & Cuijie Zhao

  2. School of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao, 066004, Hebei, China

    Hong Chen & Aiyong Liu

  3. College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001, Hebei, China

    Liwei Geng

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  1. Hong Chen
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  2. Liwei Geng
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  3. Hongdong Zhao
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  4. Cuijie Zhao
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Correspondence to Hongdong Zhao.

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Chen, H., Geng, L., Zhao, H. et al. Image recognition algorithm based on artificial intelligence. Neural Comput & Applic 34, 6661–6672 (2022). https://doi.org/10.1007/s00521-021-06058-8

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