基于卷积神经网络的图像分类算法综述  

Review of image classification algorithms based on convolutional neural network

作　　者：季长清[1,2] 高志勇 秦静[3] 汪祖民[2] JI Changqing;GAO Zhiyong;QIN Jing;WANG Zumin(College of Physical Science and Technology,Dalian University,Dalian Liaoning 116622,China;College of Information Engineering,Dalian University,Dalian Liaoning 116622,China;College of Software Engineering,Dalian University,Dalian Liaoning 116622,China)

机构地区：[1]大连大学物理科学与技术学院,辽宁大连116622 [2]大连大学信息工程学院,辽宁大连116622 [3]大连大学软件工程学院,辽宁大连116622

出　　处：《计算机应用》2022年第4期1044-1049,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62002038)。

摘　　要：卷积神经网络(CNN)是目前基于深度学习的计算机视觉领域中重要的研究方向之一。它在图像分类和分割、目标检测等的应用中表现出色,其强大的特征学习与特征表达能力越来越受到研究者的推崇。然而,CNN仍存在特征提取不完整、样本训练过拟合等问题。针对这些问题,介绍了CNN的发展、CNN经典的网络模型及其组件,并提供了解决上述问题的方法。通过对CNN模型在图像分类中研究现状的综述,为CNN的进一步发展及研究方向提供了建议。Convolutional Neural Network(CNN)is one of the important research directions in the field of computer vision based on deep learning at present.It performs well in applications such as image classification and segmentation,target detection.Its powerful feature learning and feature representation capability are admired by researchers increasingly.However,CNN still has problems such as incomplete feature extraction and overfitting of sample training.Aiming at these issues,the development of CNN,classical CNN network models and their components were introduced,and the methods to solve the above issues were provided.By reviewing the current status of research on CNN models in image classification,the suggestions were provided for further development and research directions of CNN.

关 键 词：深度学习 卷积神经网络 图像分类 特征提取 过拟合 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]