Image Data Augmentation Approaches: A Comprehensive Survey and Future Directions

Abstract

Deep learning algorithms have exhibited impressive performance across various computer vision tasks; however, the challenge of overfitting persists, especially when dealing with limited labeled data. This survey explores the mitigation of the overfitting issue through a comprehensive examination of image data augmentation techniques, which aim to enhance dataset size and diversity by introducing varied samples. The survey exclusively focuses on these techniques, presenting an insightful overview and introducing a novel taxonomy. The discussion encompasses the strengths and limitations of these techniques. Additionally, the paper provides extensive results evaluating the impact of these techniques on prevalent computer vision tasks: image classification, object detection, and semantic segmentation. The survey concludes with an examination of challenges, limitations, and potential future research directions.

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