Survey of Image Data Augmentation Techniques Based on Deep Learning
In recent years,deep learning has demonstrated excellent performance in many computer vision tasks such as image classification,object detection,and image segmentation.Deep neural networks usually rely on a large amount of training data to avoid overfitting,so excellent performance is inseparable from the support of massive image data.However,in many real-world applications,it is often difficult to obtain sufficient image data,and data collection is also expensive and time-consuming.The emergence of image data augmentation has effectively alleviated the problem of insufficient data,and as an effective way to increase the quantity,quality,and diversity of training data,data augmentation has become a necessary component for the successful appli-cation of deep learning models on image data.Understanding existing algorithms can help choose appropriate methods and develop new algorithms.This paper elaborates on the research motivation of image data augmentation,systematically classifies numerous data augmentation algorithms,analyzes each type of data augmentation algorithm in detail,and then points out some considera-tions in the design of data augmentation algorithms and their application scope.The effectiveness of data augmentation is demon-strated through three computer vision tasks,and finally,this paper summarizes and proposes some prospects for future research directions of data augmentation.
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