基于双通道交叉融合的卷积神经网络图像识别方法研究OA

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.