计算机工程与应用2019，Vol.55Issue(15)：32-37,95,7.DOI:10.3778/j.issn.1002-8331.1811-0286
改进LeNet-5网络在图像分类中的应用
Application of LeNet-5 Neural Network in Image Classification
摘要
Abstract
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.关键词
LeNet-5网络/跨连连接/Inception V1模块/图像分类Key words
LeNet-5 network/ cross-connection/ Inception V1 module/ image classification分类
信息技术与安全科学引用本文复制引用
刘金利,张培玲..改进LeNet-5网络在图像分类中的应用[J].计算机工程与应用,2019,55(15):32-37,95,7.基金项目
国家自然科学基金(No.61501175) （No.61501175）
河南省教育厅科学技术研究重点项目(No.15A510008) （No.15A510008）
河南理工大学博士基金(No.B2015-33). （No.B2015-33）
