Research on System Architecture Based
on Deep Learning Convolutional Neural
Network
Caijuan Chen and Ying Tong
(&)
Tianjin Key Laboratory of Wireless Mobile Communications and Power
Transmission, Tianjin Normal University, Tianjin, China
tongying2334@163.com
Abstract. In recent years, because deep learning technology can effectively
learn features from data, it has become a powerful technical means in the field of
image recognition. Research on image recognition can better promote the
development of artificial intelligence and computer vision. This paper has
conducted research and review of this field, introduced its important development and application, and made an attempt to promote further development in
this field. Firstly, this paper introduces the structure of the network, and then
introduces the common structural model of deep learning with CNN. The
technical methods of reducing overfitting method, neural network visualization
technology, inception structure, and transforming input images are discussed.
Finally, the problems that still need to be solved in this field and the future of
deep learning are introduced. It is pointed out that distributed computing, bit
number reduction, migration learning, image style transformation, image generation, etc., are further research directions in the field of image recognition.
Keywords: Deep learning Á Image recognition Á Convolutional neural network
1 Introduction
The wave of deep learning is surging, and related literature books are emerging one
after another. The world of science fiction movies has become a reality. Artificial
intelligence has defeated the Go champion, and mobile phones can not only understand
people’s speech (such as Microsoft’s Xiao Bing, Apple’s Siri), but also real-time
translation in video calls, the generalization of autonomous driving technology is just
around the corner. Artificial intelligence is changing our lives and gradually making
our world a new world. Such an amazing speed of development is inseparable from
deep learning technology. Deep learning has shown excellent performance in various
fields such as graphics, voice, and natural language. Image recognition technology
based on deep learning has important applications in weather forecasting, medical
imaging, navigation, and environmental monitoring. This paper has conducted research
and review of this field, introduced its important development and application, and
made an attempt to promote further development in this field. The paper first briefly
introduces the structure of the network and then outlines the structural models
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 514–522, 2020.
https://doi.org/10.1007/978-981-15-0187-6_61
on Deep Learning Convolutional Neural
Network
Caijuan Chen and Ying Tong
(&)
Tianjin Key Laboratory of Wireless Mobile Communications and Power
Transmission, Tianjin Normal University, Tianjin, China
tongying2334@163.com
Abstract. In recent years, because deep learning technology can effectively
learn features from data, it has become a powerful technical means in the field of
image recognition. Research on image recognition can better promote the
development of artificial intelligence and computer vision. This paper has
conducted research and review of this field, introduced its important development and application, and made an attempt to promote further development in
this field. Firstly, this paper introduces the structure of the network, and then
introduces the common structural model of deep learning with CNN. The
technical methods of reducing overfitting method, neural network visualization
technology, inception structure, and transforming input images are discussed.
Finally, the problems that still need to be solved in this field and the future of
deep learning are introduced. It is pointed out that distributed computing, bit
number reduction, migration learning, image style transformation, image generation, etc., are further research directions in the field of image recognition.
Keywords: Deep learning Á Image recognition Á Convolutional neural network
1 Introduction
The wave of deep learning is surging, and related literature books are emerging one
after another. The world of science fiction movies has become a reality. Artificial
intelligence has defeated the Go champion, and mobile phones can not only understand
people’s speech (such as Microsoft’s Xiao Bing, Apple’s Siri), but also real-time
translation in video calls, the generalization of autonomous driving technology is just
around the corner. Artificial intelligence is changing our lives and gradually making
our world a new world. Such an amazing speed of development is inseparable from
deep learning technology. Deep learning has shown excellent performance in various
fields such as graphics, voice, and natural language. Image recognition technology
based on deep learning has important applications in weather forecasting, medical
imaging, navigation, and environmental monitoring. This paper has conducted research
and review of this field, introduced its important development and application, and
made an attempt to promote further development in this field. The paper first briefly
introduces the structure of the network and then outlines the structural models
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 514–522, 2020.
https://doi.org/10.1007/978-981-15-0187-6_61