现代信息科技2024，Vol.8Issue(12)：47-51,55,6.DOI:10.19850/j.cnki.2096-4706.2024.12.011
基于双通道交叉融合的卷积神经网络图像识别方法研究
Research on the Image Recognition Method of Convolutional Neural Networks Based on Dual-channel Cross-fusion
摘要
Abstract
In response to the problems of insufficient feature extraction in single-channel Convolutional Neural Networks and the difficulty in training deep networks,a dual-channel cross-fusion Convolutional Neural Networks model is proposed.This model includes three feature extraction stages,with each stage performing image convolution through two separate channels.After the convolution of the two channels is completed,feature cross-fusion is carried out.After three rounds of cross-fusion,the features are input into a global average pooling layer and a fully connected layer to obtain classification results.This model is applied to image classification tasks on Cifar10,Cifar100,and Fashion-MNIST to verify its effectiveness.The results show that the dual-channel cross-fusion model can be trained on current mainstream laptops that support GPU acceleration,and it exhibits better classification performance than other similar models on datasets of the same scale.关键词
卷积神经网络/双通道/融合/分类/准确率Key words
Convolutional Neural Networks/dual-channel/fusion/classification/accuracy分类
信息技术与安全科学引用本文复制引用
黄曼曼,王松林,周正贵,侯秀丽..基于双通道交叉融合的卷积神经网络图像识别方法研究[J].现代信息科技,2024,8(12):47-51,55,6.基金项目
安徽省质量工程项目(2021zyyh021,2021jyxm0461) （2021zyyh021,2021jyxm0461）
安徽商贸职业技术学院重点科研项目(2021KZZ01) （2021KZZ01）
安徽省教学示范课(2020SJ1069) （2020SJ1069）
安徽省精品课程(2022jpkc048) （2022jpkc048）
