深度学习在图像识别中的研究与应用

Yinglong Li
{"title":"深度学习在图像识别中的研究与应用","authors":"Yinglong Li","doi":"10.1109/ICPECA53709.2022.9718847","DOIUrl":null,"url":null,"abstract":"Deep learning is a technical tool with broad application prospects and has an important role in the field of image recognition. In view of the theoretical value and practical significance of image recognition technology in promoting the development of computer vision and artificial intelligence, this paper will review and study the application of deep learning in image recognition. This paper first outlines the development of icon recognition technology, and then introduces three main learning models in deep learning: convolutional neural networks, recurrent neural networks, and generative adversarial networks, and provides a comparative analysis of these three learning models. Finally, the research results of deep learning image recognition application fields, such as face recognition, medical image recognition, and remote sensing image classification, are analyzed and discussed. This paper also analyze the development trend of deep learning in the field of image recognition, and conclude that the future development direction is the effective recognition of video images and the theoretical strengthening of models.","PeriodicalId":244448,"journal":{"name":"2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"50","resultStr":"{\"title\":\"Research and Application of Deep Learning in Image Recognition\",\"authors\":\"Yinglong Li\",\"doi\":\"10.1109/ICPECA53709.2022.9718847\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep learning is a technical tool with broad application prospects and has an important role in the field of image recognition. In view of the theoretical value and practical significance of image recognition technology in promoting the development of computer vision and artificial intelligence, this paper will review and study the application of deep learning in image recognition. This paper first outlines the development of icon recognition technology, and then introduces three main learning models in deep learning: convolutional neural networks, recurrent neural networks, and generative adversarial networks, and provides a comparative analysis of these three learning models. Finally, the research results of deep learning image recognition application fields, such as face recognition, medical image recognition, and remote sensing image classification, are analyzed and discussed. This paper also analyze the development trend of deep learning in the field of image recognition, and conclude that the future development direction is the effective recognition of video images and the theoretical strengthening of models.\",\"PeriodicalId\":244448,\"journal\":{\"name\":\"2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"50\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPECA53709.2022.9718847\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPECA53709.2022.9718847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 50

摘要

深度学习是一种具有广阔应用前景的技术工具,在图像识别领域有着重要的作用。鉴于图像识别技术在推动计算机视觉和人工智能发展方面的理论价值和现实意义,本文将对深度学习在图像识别中的应用进行综述和研究。本文首先概述了图标识别技术的发展,然后介绍了深度学习中的三种主要学习模型:卷积神经网络、循环神经网络和生成对抗网络,并对这三种学习模型进行了比较分析。最后,对人脸识别、医学图像识别、遥感图像分类等深度学习图像识别应用领域的研究成果进行了分析和讨论。本文还分析了深度学习在图像识别领域的发展趋势,认为未来的发展方向是视频图像的有效识别和模型的理论强化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research and Application of Deep Learning in Image Recognition
Deep learning is a technical tool with broad application prospects and has an important role in the field of image recognition. In view of the theoretical value and practical significance of image recognition technology in promoting the development of computer vision and artificial intelligence, this paper will review and study the application of deep learning in image recognition. This paper first outlines the development of icon recognition technology, and then introduces three main learning models in deep learning: convolutional neural networks, recurrent neural networks, and generative adversarial networks, and provides a comparative analysis of these three learning models. Finally, the research results of deep learning image recognition application fields, such as face recognition, medical image recognition, and remote sensing image classification, are analyzed and discussed. This paper also analyze the development trend of deep learning in the field of image recognition, and conclude that the future development direction is the effective recognition of video images and the theoretical strengthening of models.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信