Contextual copy-paste data augmentation for object detection

Liuying Zhang; Xikun Wang

doi:10.1117/12.3011809

8 November 2023 Contextual copy-paste data augmentation for object detection

Liuying Zhang, Xikun Wang

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129232I (2023) https://doi.org/10.1117/12.3011809
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

From the perspective of increasing the number of objects in the object detection dataset and ensuring their quality, this paper proposes contextual copy-paste data augmentation for object detection, which generates new images that match the objective situation by copying and pasting objects in the dataset. We train a object background classifier based on the background features of the same class objects. Use the trained classifier to classify the image background and paste the object that matches the background based on the classification results. The experimental results on PASCAL VOC 2012 showed that the algorithm we proposed effectively increased the number of objects in the dataset. The mean average precision of the four selected object detectors on the PASCAL VOC 2012 test set increased by about 0.9%, proving the effectiveness of our proposed data augmentation algorithm for object detection tasks.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liuying Zhang and Xikun Wang "Contextual copy-paste data augmentation for object detection", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129232I (8 November 2023); https://doi.org/10.1117/12.3011809

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