Image recognition system based on deep learning technology

Research Article
Open access

Image recognition system based on deep learning technology

Zetai Lin 1*
  • 1 School of Computer Science and Electronic Engineering, Essex University, Colchester, Britain, CO4 3SQ    
  • *corresponding author zl19012@essex.ac.uk
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230875
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

In recent years, people have been able to easily obtain a large amount of visual information through various devices, but not all of these visual information are useful, and there is a lot of useless information mixed with them, which makes it difficult for people to identify and track the target parent in the picture. Due to its various characteristics, deep learning technology can quickly obtain information from massive information and compare, analyze and utilize it, so it has become the key to solving the problem of image recognition and tracking. This paper will summarize the existing literature, focus on the structure of the image detection system based on deep learning technology, explain its basic principles, and briefly introduce the basis of deep learning technology - neural network. This paper finds that there are many technical routes for image recognition systems based on deep learning technology, each of which has its own advantages and disadvantages.

Keywords:

Neural Networks, Deep Learning, Image Recognition.

Lin,Z. (2023). Image recognition system based on deep learning technology. Applied and Computational Engineering,6,512-515.
Export citation

References

[1]. Lu Hongtao, Zhang Qinchuan. A review of the application of depth convolution neural network in computer vision [J]. Data acquisition and processing,2016,31(01):1-17.DOI:10.16337/j.1004-9037.2016.01.001.

[2]. Lowe D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.

[3]. Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent. Why Does Unsupervised Pre-training Help Deep Learning?. Journal of Machine Learning Research, 2010, 11: 625-660

[4]. Zhang Junyang, Wang Huili, Guo Yang, Hu Xiao. A review of research related to deep learning [J]. Computer application research, 2018,35(07):1921-1928+1936.

[5]. Guan Q, Wang Y, Ping B, et al. Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study[J]. Journal of Cancer, 2019, 10(20): 4876.

[6]. Chang Liang, Deng Xiaoming, Zhou Mingquan, Wu Zhongke, Yuan Ye, Yang Shuo, Wang Hong'an. Convolution Neural Network in Image Understanding [J]. Journal of Automation, 2016,42(09):1300-1312.DOI:10.16383/j.aas.2016.c150800.

[7]. Lin Chengchuang, Chunchun, Zhao Gansen, Yang Zhirong, Peng Jing, Chen Shaojie, Huang Runhua, Li Zhuangwei, Yi Xusheng, Du Jiahua, Li Shuangyin, Luo Haoyu, Fan Xiaomao, Chen Bingbing. Overview of image data enhancement in machine vision applications [J]. Computer Science and Exploration,2021,15(04):583-611.

Cite this article

Lin,Z. (2023). Image recognition system based on deep learning technology. Applied and Computational Engineering,6,512-515.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Lu Hongtao, Zhang Qinchuan. A review of the application of depth convolution neural network in computer vision [J]. Data acquisition and processing,2016,31(01):1-17.DOI:10.16337/j.1004-9037.2016.01.001.

[2]. Lowe D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.

[3]. Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent. Why Does Unsupervised Pre-training Help Deep Learning?. Journal of Machine Learning Research, 2010, 11: 625-660

[4]. Zhang Junyang, Wang Huili, Guo Yang, Hu Xiao. A review of research related to deep learning [J]. Computer application research, 2018,35(07):1921-1928+1936.

[5]. Guan Q, Wang Y, Ping B, et al. Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study[J]. Journal of Cancer, 2019, 10(20): 4876.

[6]. Chang Liang, Deng Xiaoming, Zhou Mingquan, Wu Zhongke, Yuan Ye, Yang Shuo, Wang Hong'an. Convolution Neural Network in Image Understanding [J]. Journal of Automation, 2016,42(09):1300-1312.DOI:10.16383/j.aas.2016.c150800.

[7]. Lin Chengchuang, Chunchun, Zhao Gansen, Yang Zhirong, Peng Jing, Chen Shaojie, Huang Runhua, Li Zhuangwei, Yi Xusheng, Du Jiahua, Li Shuangyin, Luo Haoyu, Fan Xiaomao, Chen Bingbing. Overview of image data enhancement in machine vision applications [J]. Computer Science and Exploration,2021,15(04):583-611.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.