Zeng Wu
Engineering Training Center, Putian University, Putian 351100, China.
Math Biosci Eng. 2024 Jun 12;21(6):6190-6224. doi: 10.3934/mbe.2024272.
In recent years, deep learning (DL) techniques have achieved remarkable success in various fields of computer vision. This progress was attributed to the vast amounts of data utilized to train these models, as they facilitated the learning of more intricate and detailed feature information about target objects, leading to improved model performance. However, in most real-world tasks, it was challenging to gather sufficient data for model training. Insufficient datasets often resulted in models prone to overfitting. To address this issue and enhance model performance, generalization ability, and mitigate overfitting in data-limited scenarios, image data augmentation methods have been proposed. These methods generated synthetic samples to augment the original dataset, emerging as a preferred strategy to boost model performance when data was scarce. This review first introduced commonly used and highly effective image data augmentation techniques, along with a detailed analysis of their advantages and disadvantages. Second, this review presented several datasets frequently employed for evaluating the performance of image data augmentation methods and examined how advanced augmentation techniques can enhance model performance. Third, this review discussed the applications and performance of data augmentation techniques in various computer vision domains. Finally, this review provided an outlook on potential future research directions for image data augmentation methods.
近年来，深度学习（DL）技术在计算机视觉的各个领域都取得了显著成功。这一进展归因于用于训练这些模型的大量数据，因为它们有助于学习有关目标对象的更复杂、更详细的特征信息，从而提高模型性能。然而，在大多数实际任务中，为模型训练收集足够的数据具有挑战性。数据集不足往往导致模型容易出现过拟合。为了解决这个问题，提高模型性能、泛化能力，并减轻数据受限场景中的过拟合，人们提出了图像数据增强方法。这些方法生成合成样本以扩充原始数据集，成为在数据稀缺时提高模型性能的首选策略。本综述首先介绍了常用且高效的图像数据增强技术，并详细分析了它们的优缺点。其次，本综述介绍了几个常用于评估图像数据增强方法性能的数据集，并研究了先进的增强技术如何提高模型性能。第三，本综述讨论了数据增强技术在各个计算机视觉领域的应用和性能。最后，本综述对图像数据增强方法未来潜在的研究方向进行了展望。
