Deep learning has yielded remarkable achievements in many computer vision tasks. However, deep neural networks typically require a large amount of training data to prevent overfitting. In practical applications, labeled data may be extremely limited. Thus, data augmentation has become an effective way to enhance the adequacy and diversity of training data and is also a necessary link for the successful application of deep learning models to image data. This study systematically reviews different image data augmentation methods and proposes a new classification method to provide a fresh perspective for studying image data augmentation. The advantages and limitations of various data augmentation methods are introduced from different categories, and the solution ideas and application values of these methods are elaborated. In addition, commonly used public datasets and performance evaluation indicators in three typical computer vision tasks of semantic segmentation, image classification, and object detection are presented. Experimental comparative analysis of data augmentation methods is conducted on these three tasks. Finally, the challenges and future development trends currently faced by data augmentation are discussed.