基于卷积神经网络在图像识别中的应用研究
Research on the Application of Convolutional Neural Networks in Image Recognition
罗富贵 1宋倩 1覃运初 1施运应1
作者信息
- 1. 河池学院 大数据与计算机学院,广西 河池 546300
- 折叠
摘要
文章主要介绍了卷积神经网络的深度学习模型,其在图像处理方面表现突出并且具有端到端的学习能力,因此,近年来CNN在图像识别领域一直很热门.文章总结和探讨了基于卷积神经网络的图像识别研究,包括介绍了该模型的基本结构和工作原理,比较了常用卷积神经网络模型的优缺点,还探讨了卷积神经网络在人脸识别、医学图像识别、交通识别、字符识别等领域的应用,最后,文章对神经卷积网络在图像识别方面需要解决的问题以及未来的发展方向进行了讨论,以期为图像识别技术的进一步研究提供相应的参考.
Abstract
This article mainly introduces the deep learning model of Convolutional Neural Networks,which has shown remarkable performance in image processing and has end-to-end learning capability.Therefore,CNN has been popular in the field of image recognition in recent years.The article summarizes and explores research on image recognition based on CNN,including introducing the basic structure and working principle of the model,comparing the advantages and disadvantages of commonly used CNN models,and exploring the applications of CNN in fields such as face recognition,medical image recognition,traffic recognition,and character recognition.Lastly,the article also discusses the challenges that need to be addressed and future development directions in neural convolutional networks for image recognition,aiming to provide reference for the further research of image recognition technology.
关键词
卷积神经网络/图像识别/网络结构/人脸识别Key words
convolutional neural networks/image recognition/network structure/face recognition引用本文复制引用
出版年
2024
NETL
NSTL
万方数据