Data Augmentation For Small Object using Fast AutoAugment

TL;DR

This paper introduces a fast AutoAugment-based data augmentation method that significantly improves small object detection performance, achieving a 20% boost on the DOTA dataset.

Contribution

The paper presents a novel, efficient data augmentation approach specifically designed to enhance small object detection in computer vision.

Findings

20% performance improvement on DOTA dataset

Fast AutoAugment effectively finds optimal augmentation policies

Enhanced detection accuracy for small objects

Abstract

In recent years, there has been tremendous progress in object detection performance. However, despite these advances, the detection performance for small objects is significantly inferior to that of large objects. Detecting small objects is one of the most challenging and important problems in computer vision. To improve the detection performance for small objects, we propose an optimal data augmentation method using Fast AutoAugment. Through our proposed method, we can quickly find optimal augmentation policies that can overcome degradation when detecting small objects, and we achieve a 20% performance improvement on the DOTA dataset.