Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (4): 583-611.DOI: 10.3778/j.issn.1673-9418.2102015

• Surveys and Frontiers • Previous Articles     Next Articles

Review of Image Data Augmentation in Computer Vision

LIN Chengchuang, SHAN Chun, ZHAO Gansen, et al   

  1. 1. School of Computer Science, South China Normal University, Guangzhou 510631, China
    2. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, China
    3. Norwegian University of Science and Technology, Trondheim 17491, Norway
    4. Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou 510631, China
    5. South China Normal University & VeChain Joint Lab on BlockChain Technology and Application, Guangzhou 510631, China
    6. School of Statistics and Mathematics, Guangdong University of Finance and Economics, Guangzhou 510320, China
  • Online:2021-04-01 Published:2021-04-02



  1. 1. 华南师范大学 计算机学院,广州 510631
    2. 广东技术师范大学 电子与信息学院,广州 510665
    3. 挪威科技大学,挪威 特隆赫姆 17491
    4. 广州市云计算安全与测评技术重点实验室,广州 510631
    5. 华南师范大学唯链区块链技术与应用联合实验室,广州 510631
    6. 广东财经大学 统计与数学学院,广州 510320


Deep learning is a promising solution for computer vision at present. To solve the computer vision problem, it requires massive and high-quality image training datasets. Collecting and accurately labeling image datasets is a very time-consuming and expensive process. As computer vision applications become more widespread, it makes this problem even more pronounced. Image augmentation technologies are technical methods to effectively solve the problem of deep learning training under the condition of small-scale or low-quality training data. These technologies are continually accompanied with the development of deep learning and computer vision. This paper first reviews these image augmentation researches from the perspective of augmentation objects, operation spaces, label processing methods, and augmentation strategies and then concludes corresponding paradigms of current image data augmentation methods. After that, this paper proposes a taxonomy for current image data augmentation guided by the above paradigms, and reviews corresponding representative methods of each image data augmentation category. Finally, this paper makes conclusions on existing image data augmentation, points out the problems existing in the current image augmentation research and presents promising directions for future research.

Key words: deep learning, computer vision, image augmentation, data augmentation, image enhancement



关键词: 深度学习, 计算机视觉, 图像增广, 数据增广, 图像增强