Copy and restricted paste: data augmentation for small object detection in specific scenes

Yunhe Hao; Wang Luo; Yingjie Li; Beibei Zhang; Jia Bei

doi:10.1117/12.2658697

22 December 2022 Copy and restricted paste: data augmentation for small object detection in specific scenes

Yunhe Hao, Wang Luo, Yingjie Li, Beibei Zhang, Jia Bei

Proceedings Volume 12508, International Symposium on Artificial Intelligence and Robotics 2022; 125080H (2022) https://doi.org/10.1117/12.2658697
Event: Seventh International Symposium on Artificial Intelligence and Robotics 2022, 2022, Shanghai, China

Abstract

Small object detection has been a difficult task because of small area, low resolution, few available features and many other problems. In order to improve the performance of small object detection, a classical augmentation method, which copies and pastes small objects to the image, is usually adopted. However, in some specific scenes, small objects cannot be pasted completely randomly on the picture without any area restriction. In this paper, to solve the task of small object detection in specific scenes, on the basis of the copy-paste augmentation method, we further design three strategies to restrict the paste position of the copied object to the target area of the image. In this way, the augmented image is more realistic to the scenario, which can improve the performance of small object detection. We conduct experiments on different object detection methods, and validate that in contrast to two-stage object detection methods, our copy-and-restricted-paste augmentation strategy is more suitable for one-stage object detection methods.

Citation Download Citation

Yunhe Hao, Wang Luo, Yingjie Li, Beibei Zhang, and Jia Bei "Copy and restricted paste: data augmentation for small object detection in specific scenes", Proc. SPIE 12508, International Symposium on Artificial Intelligence and Robotics 2022, 125080H (22 December 2022); https://doi.org/10.1117/12.2658697

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