基于卷积神经网络的宠物犬种类识别研究

Yanmei Liu, Yuda Chen
{"title":"基于卷积神经网络的宠物犬种类识别研究","authors":"Yanmei Liu, Yuda Chen","doi":"10.1109/ISCID51228.2020.00068","DOIUrl":null,"url":null,"abstract":"At present, the related research of image recognition is getting more and more popular, but in the process of research, the recognition effect of the model is not good enough and it is easy to misrecognize. This paper proposes an improvement solution for the above problems on the selection and construction of the model structure and the adjustment and optimization methods in the model training process. The final result achieves 96% recognition accuracy on the data composed of 9092 pet dog images. It is proved that the model by choosing deep-level network model and adopts regularization method to adjusting and optimizing the model, which can effectively improve model for image recognition effect.","PeriodicalId":236797,"journal":{"name":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Research on Pet Dog Species Identification Based on Convolution Neural Network\",\"authors\":\"Yanmei Liu, Yuda Chen\",\"doi\":\"10.1109/ISCID51228.2020.00068\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"At present, the related research of image recognition is getting more and more popular, but in the process of research, the recognition effect of the model is not good enough and it is easy to misrecognize. This paper proposes an improvement solution for the above problems on the selection and construction of the model structure and the adjustment and optimization methods in the model training process. The final result achieves 96% recognition accuracy on the data composed of 9092 pet dog images. It is proved that the model by choosing deep-level network model and adopts regularization method to adjusting and optimizing the model, which can effectively improve model for image recognition effect.\",\"PeriodicalId\":236797,\"journal\":{\"name\":\"2020 13th International Symposium on Computational Intelligence and Design (ISCID)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 13th International Symposium on Computational Intelligence and Design (ISCID)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCID51228.2020.00068\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 13th International Symposium on Computational Intelligence and Design (ISCID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCID51228.2020.00068","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

目前,图像识别的相关研究越来越受欢迎,但在研究过程中,模型的识别效果不够好,容易出现误识别。针对上述问题,本文从模型结构的选择与构建以及模型训练过程中的调整与优化方法等方面提出了改进方案。最终结果在9092张宠物狗图像组成的数据上实现了96%的识别准确率。实验证明,该模型通过选择深层网络模型并采用正则化方法对模型进行调整和优化,可以有效地提高模型对图像识别的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Pet Dog Species Identification Based on Convolution Neural Network
At present, the related research of image recognition is getting more and more popular, but in the process of research, the recognition effect of the model is not good enough and it is easy to misrecognize. This paper proposes an improvement solution for the above problems on the selection and construction of the model structure and the adjustment and optimization methods in the model training process. The final result achieves 96% recognition accuracy on the data composed of 9092 pet dog images. It is proved that the model by choosing deep-level network model and adopts regularization method to adjusting and optimizing the model, which can effectively improve model for image recognition effect.
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:604180095
Book学术官方微信