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基于卷积神经网络的犬类识别技术研究

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运用卷积神经网络方法对犬类识别问题提供一种有效的解决方案.首先数据集来源于斯坦福大学的120种犬类标准数据集,其次搭建了Alexnet与VGG16两种卷积神经网络的模型,经训练测试后两种卷积神经网络模型均可识别数据集中的犬类,最后通过验证集实验对比得出,搭建的VGG16卷积神经网络模型识别效果优于Alexnet卷积神经网络模型,其验证集的识别率达到了89.17%.
Research on Dog Recognition Technology Based on Convolutional Neural Networks
It uses convolutional neural networks in the paper to provide an effective solution for dog recog-nition problems.Firstly,the dataset is sourced from the standard dataset of 120 dog breeds at Stanford U-niversity.Secondly,it constructs models of Alexnet and VGG16 convolutional neural networks.After train-ing and testing,both convolutional neural network models can recognize dogs in the dataset.Finally,through experimental comparison on the validation set,it is found that the VGG16 convolutional neural network model constructed in this paper has better recognition performance than the Alexnet convolutional neural network model,with a recognition rate of 89.17% on the validation set.

dog recognitionconvolutional neural networkAlexnet modelVGG16 model

宗兆星、杨燕婷、余国庆、李冬梅、刘光宇

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大理大学 工程学院,云南 大理 671003

上海交通大学 海洋智能装备与系统教育部实验室,上海 200030

犬类识别 卷积神经网络 Alexnet模型 VGG16模型

海洋智能装备与系统教育部重点实验室开放项目云南省教育厅科学研究项目云南省教育厅科学研究项目

MIES-2023-022024Y8552024Y851

2024

蚌埠学院学报
蚌埠学院

蚌埠学院学报

影响因子:0.231
ISSN:2095-297X
年,卷(期):2024.13(5)
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