Contextual copy-paste data augmentation for object detection
zhang liuying, Wang Xikun · 2023
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.