  Deep learning has achieved remarkable results in many computer vision tasks.
Deep neural networks typically rely on large amounts of training data to avoid
overfitting. However, labeled data for real-world applications may be limited.
By improving the quantity and diversity of training data, data augmentation has
become an inevitable part of deep learning model training with image data.
  As an effective way to improve the sufficiency and diversity of training
data, data augmentation has become a necessary part of successful application
of deep learning models on image data. In this paper, we systematically review
different image data augmentation methods. We propose a taxonomy of reviewed
methods and present the strengths and limitations of these methods. We also
conduct extensive experiments with various data augmentation methods on three
typical computer vision tasks, including semantic segmentation, image
classification and object detection. Finally, we discuss current challenges
faced by data augmentation and future research directions to put forward some
useful research guidance.
