Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (3): 512-528.DOI: 10.3778/j.issn.1673-9418.2107056

• Surveys and Frontiers • Previous Articles     Next Articles

Research Progress of Lightweight Neural Network Convolution Design

MA Jinlin1,2,+(), ZHANG Yu1,2, MA Ziping3, MAO Kaiji1,2   

  1. 1. School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
    2. Key Laboratory of Intelligent Processing of Computer Images and Graphics of National Ethnic Affairs Commission of the PRC, Yinchuan 750021, China
    3. School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China
  • Received:2021-07-14 Revised:2021-09-29 Online:2022-03-01 Published:2021-09-29
  • About author:MA Jinlin, born in 1976, Ph.D., associate professor. His research interests include computer vision, deep learning and machine learning.
    ZHANG Yu, born in 1994, M.S. candidate. Her research interests include computer vision, deep learning and machine learning.
    MA Ziping, born in 1977, Ph.D., associate professor. Her research interest is computer graphics.
    MAO Kaiji, born in 1996, M.S. candidate. His research interests include computer vision, deep learning and machine learning.
  • Supported by:
    Basic Scientific Research Program in Central Universities of Northern Minzu University(2021KJCX09);Basic Scientific Research Program in Central Universities of Northern Minzu University(FWNX21);Basic Scientific Research Program in Central Universities of Northern Minzu University(ZDZX201801);Natural Science Foundation of Ningxia(2020AAC3215);Project of “Computer Vision and Virtual Reality” Innovation Team of North Minzu University;National Natural Science Foundation of China(61462002)

轻量化神经网络卷积设计研究进展

马金林1,2,+(), 张裕1,2, 马自萍3, 毛凯绩1,2   

  1. 1.北方民族大学 计算机科学与工程学院,银川 750021
    2.图像图形智能处理国家民委重点实验室,银川 750021
    3.北方民族大学 数学与信息科学学院,银川 750021
  • 通讯作者: + E-mail: 624160@163.com
  • 作者简介:马金林(1976—),男,宁夏青铜峡人,博士,副教授,主要研究方向为计算机视觉、深度学习、机器学习。
    张裕(1994—),女,安徽太和县人,硕士研究生,主要研究方向为计算机视觉、深度学习、机器学习。
    马自萍(1977—),女,宁夏吴忠人,博士,副教授,主要研究方向为计算机图形学。
    毛凯绩(1996—),男,黑龙江鹤岗人,硕士研究生,主要研究方向为计算机视觉、深度学习、机器学习。
  • 基金资助:
    北方民族大学中央高校基本科研业务费专项(2021KJCX09);北方民族大学中央高校基本科研业务费专项(FWNX21);北方民族大学中央高校基本科研业务费专项(ZDZX201801);宁夏自然科学基金(2020AAC3215);北方民族大学“计算机视觉与虚拟现实”创新团队项目;国家自然科学基金(61462002)

Abstract:

Traditional neural networks have the disadvantages of over-reliance on hardware resources and high requirements for application equipment performance. Therefore, they cannot be deployed on edge devices and mobile terminals with limited computing power. The application development of artificial intelligence technology is limited to a certain extent. However, with the advent of the technological age, artificial intelligence, which is affected by user requirements, urgently needs to be able to successfully perform operations such as computer vision applications on portable devices. For this reason, this paper takes the convolution of popular lightweight neural networks in recent years as the research object. Firstly, by introducing the concept of lightweight neural network, the development status of lightweight neural networks and the problems faced by convolution in the network are introduced. Secondly, the convolution is divided into three aspects: lightweight of convolution structure, lightweight of convolution module and lightweight of convolution operation, specifically through the study of the convolution design in various lightweight neural network models, the lightweight effects of different convolutions are demonstrated, and the advantages and disadvantages of the optimization methods are explained. Finally, the main ideas and usage methods of all lightweight model convolutional design in this paper are summarized and analyzed, and their possible future development is prospected.

Key words: convolution, lightweight neural network, network model, computer vision, deep learning

摘要:

传统神经网络具有过度依赖硬件资源和对应用设备性能要求较高的缺点,因此无法部署于算力有限的边缘设备和移动终端上,人工智能技术的应用发展在一定程度上受到了限制。然而,随着科技时代的到来,受用户需求影响的人工智能迫切需要在便携式设备上能成功进行如计算机视觉应用等方面的操作。为此,以近几年流行的轻量化神经网络中的卷积部分为研究对象,详细比对了各类轻量化模型中卷积构成方式的区别,并针对卷积设计的主要思路和特点进行了较为详细的阐述。首先,通过引入轻量化神经网络的概念,介绍了轻量化神经网络的发展现状和网络中卷积方面所面临的问题;然后,将卷积分为卷积结构轻量化、卷积模块轻量化和卷积运算轻量化三方面进行介绍,具体通过对各类轻量化神经网络模型中卷积设计的研究,来展示不同卷积的轻量化效果,并对其中优化方法的优缺点进行阐述;最后,对文中所有轻量化模型卷积设计的主要思路和使用方式进行了总结分析,并对其未来的可能性发展进行了展望。

关键词: 卷积, 轻量化神经网络, 网络模型, 计算机视觉, 深度学习

CLC Number: