Alomar Khaled, Aysel Halil Ibrahim, Cai Xiaohao
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
J Imaging. 2023 Feb 17;9(2):46. doi: 10.3390/jimaging9020046.
In the past decade, deep neural networks, particularly convolutional neural networks, have revolutionised computer vision. However, all deep learning models may require a large amount of data so as to achieve satisfying results. Unfortunately, the availability of sufficient amounts of data for real-world problems is not always possible, and it is well recognised that a paucity of data easily results in overfitting. This issue may be addressed through several approaches, one of which is data augmentation. In this paper, we survey the existing data augmentation techniques in computer vision tasks, including segmentation and classification, and suggest new strategies. In particular, we introduce a way of implementing data augmentation by using local information in images. We propose a parameter-free and easy to implement strategy, the random local rotation strategy, which involves randomly selecting the location and size of circular regions in the image and rotating them with random angles. It can be used as an alternative to the traditional rotation strategy, which generally suffers from irregular image boundaries. It can also complement other techniques in data augmentation. Extensive experimental results and comparisons demonstrated that the new strategy consistently outperformed its traditional counterparts in, for example, image classification.
在过去十年中，深度神经网络，尤其是卷积神经网络，彻底改变了计算机视觉。然而，所有深度学习模型可能都需要大量数据才能取得令人满意的结果。不幸的是，对于现实世界的问题而言，并不总是能够获得足够的数据量，而且人们普遍认识到数据匮乏很容易导致过拟合。这个问题可以通过几种方法来解决，其中之一就是数据增强。在本文中，我们调研了计算机视觉任务（包括分割和分类）中现有的数据增强技术，并提出了新的策略。特别是，我们介绍了一种利用图像中的局部信息来实现数据增强的方法。我们提出了一种无参数且易于实现的策略——随机局部旋转策略，该策略涉及在图像中随机选择圆形区域的位置和大小，并以随机角度旋转它们。它可以作为传统旋转策略的替代方法，传统旋转策略通常存在图像边界不规则的问题。它还可以补充数据增强中的其他技术。大量的实验结果和比较表明，新策略在例如图像分类等方面始终优于传统方法。
