基于卷积神经网络的图像识别综述

A Review of Image Recognition Based on Convolutional Neural Network

随着工业化进程的迅猛发展,产生了大量的图像信息,传统的图像识别技术难以处理如此庞大的图像数据以及满足速度和精度上的要求,大数据及深度学习技术应运而生,基于卷积神经网络的图像识别方法成为目前图像识别的主流算法。文中首先介绍了传统图像识别技术及存在的问题,引入了卷积神经网络的深度学习方法,重点说明了卷积网络中间层的结构和特点,然后介绍图像识别中经典的卷积神经网络模型及相互间的区别,最后简要综述卷积神经网络在图像识别中的应用,指出了有监督的卷积网络学习缺点及无监督学习的研究方向。

With the rapid development of industrialization,a large amount of image information is generated.Traditional image recognition technology is difficult to process such a huge amount of data and meet the requirements of speed and accuracy,and image recognition algorithms based on convolutional neural network has become the mainstream of image recognition big data and with the emergence of deep learning technology.This paper introduces the traditional image recognition technology and the existing problems,the deep learning method of convolutional neural network,the structure and characteristics of the middle layer of the convolution network,and the classical convolutional neural network models and their differences in image recognition.The application of convolutional neural network in image recognition is briefly reviewed,and the shortcomings of supervised convolution network learning and the research direction of unsupervised learning are pointed out.