改进LeNet-5网络在图像分类中的应用

LeNet-5卷积神经网络（CNN）虽然在手写数字识别上取得很好的分类效果，但在具有复杂纹理特征的数据集上分类精度不高。为提高网络在复杂纹理特征图像上分类的正确率，提出一种改进的LeNet-5网络结构。引入跨连思想，充分利用网络提取的低层次特征；把Inception V1模块嵌入LeNet-5卷积神经网络，提取图像的多尺度特征；输出层使用softmax函数对图像进行分类。在Cifar-10和Fashion MNIST数据集上进行的实验结果表明，改进的卷积神经网络在复杂纹理特征数据集上具有很好的分类能力。

Application of LeNet-5 Neural Network in Image Classification

Although the LeNet-5 Convolutional Neural Network（CNN） achieves good classification results in handwritten digit recognition, the classification accuracy is not high on datasets with complex texture features. In order to improve the accuracy of network class-ification on complex texture feature images, an improved LeNet-5 network structure is proposed. The idea of cross-connection is introduced to make full use of the low-level features of network extraction. The Inception V1 module is embedded in the LeNet-5 convolutional neural network to extract multi-scale features of the image. The output layer uses the softmax function to classify the image. Experimental results on the Cifar-10 and Fashion MNIST dataset show that the improved convolutional neural network has good classification ability on complex texture feature datasets.