计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (1): 150-167.doi: 10.11896/jsjkx.230500103

• 计算机图形学&多媒体 • 上一篇    下一篇

基于深度学习的图像数据增强研究综述

孙书魁1,2, 范菁1,2, 孙中强3, 曲金帅1,2, 代婷婷1,2   

  1. 1 云南民族大学电气信息工程学院 昆明650000
    2 云南民族大学云南省高校信息与通信安全灾备重点实验室 昆明650000
    3 苏州大学计算机科学与技术学院 江苏 苏州215000
  • 收稿日期:2023-05-16 修回日期:2023-06-20 出版日期:2024-01-15 发布日期:2024-01-12
  • 通讯作者: 范菁(fanjing@ymu.edu.cn)
  • 作者简介:(shukuisun@163.com)
  • 基金资助:
    国家自然科学基金(61540063);云南省教育厅科学研究基金(2023Y0499);云南民族大学硕士研究生科研创新基金(2022SKY004)

Survey of Image Data Augmentation Techniques Based on Deep Learning

SUN Shukui1,2, FAN Jing1,2, SUN Zhongqing3, QU Jinshuai1,2, DAI Tingting1,2   

  1. 1 School of Electrical and Information Technology,Yunnan Minzu University,Kunming 650000,China
    2 University Laboratory of Information and Communication on Security Backup and Recovery in Yunnan Province,Yunnan Minzu University,Kunming 650000,China
    3 School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215000,China
  • Received:2023-05-16 Revised:2023-06-20 Online:2024-01-15 Published:2024-01-12
  • About author:SUN Shukui,born in 1996,postgra-duate,is a student member of CCF(No.K5536G).His main research interests include image generation and computer vision.
    FAN Jing,born in 1976,Ph.D,professor,master supervisor,is a senior member of CCF(No.14172M).Her main research interests include machine lear-ning and computer vision.
  • Supported by:
    National Natural Science Foundation of China(61540063),Scientific Research Fundation of the Education Department of Yunnan Province,China(2023Y0499) and Yunnan Minzu University Master’s Research and Innovation Fund Project(2022SKY004).

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摘要： 近年来,深度学习在图像分类、目标检测、图像分割等诸多计算机视觉任务中都取得了出色的性能表现。深度神经网络通常依靠大量的训练数据来避免过拟合,因此,出色的性能背后离不开海量图像数据的支持。但在很多实际应用场景中,通常很难获取到足够的图像数据,并且数据的收集也是昂贵且耗时的。图像数据增强的出现很好地缓解了数据不足的问题,作为增加训练数量、提升数据质量和多样性的有效途径,数据增强已成为深度学习模型在图像数据上成功应用的必要组成部分,理解现有算法有助于选择适合的方法以及开发新算法。文中阐述了图像数据增强的研究动机,对众多的数据增强算法进行了系统分类,详细分析了每一类数据增强算法;随后指出数据增强算法设计时的一些注意事项及其应用范围,并通过3种计算机视觉任务证明了数据增强的有效性;最后总结全文并对数据增强未来的研究方向进行展望。

关键词: 图像增强, 深度学习, 数据增强, 计算机视觉, 人工智能, 生成对抗网络

Abstract: In recent years,deep learning has demonstrated excellent performance in many computer vision tasks such as image classification,object detection,and image segmentation.Deep neural networks usually rely on a large amount of training data to avoid overfitting,so excellent performance is inseparable from the support of massive image data.However,in many real-world applications,it is often difficult to obtain sufficient image data,and data collection is also expensive and time-consuming.The emergence of image data augmentation has effectively alleviated the problem of insufficient data,and as an effective way to increasethe quantity,quality,and diversity of training data,data augmentation has become a necessary component for the successful application of deep learning models on image data.Understanding existing algorithms can help choose appropriate methods and develop new algorithms.This paper elaborates on the research motivation of image data augmentation,systematically classifies numerous data augmentation algorithms,analyzes each type of data augmentation algorithm in detail,and then points out some considerations in the design of data augmentation algorithms and their application scope.The effectiveness of data augmentation is demonstrated through three computer vision tasks,and finally,this paper summarizes and proposes some prospects for future research directions of data augmentation.

Key words: Image augmentation, Deep learning, Data augmentation, Computer vision, Artificial intelligence, Generative adversarial network

中图分类号: 

  • TP181
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