  Data augmentation has always been an effective way to overcome overfitting
issue when the dataset is small. There are already lots of augmentation
operations such as horizontal flip, random crop or even Mixup. However, unlike
image classification task, we cannot simply perform these operations for object
detection task because of the lack of labeled bounding boxes information for
corresponding generated images. To address this challenge, we propose a
framework making use of Generative Adversarial Networks(GAN) to perform
unsupervised data augmentation. To be specific, based on the recently supreme
performance of YOLOv4, we propose a two-step pipeline that enables us to
generate an image where the object lies in a certain position. In this way, we
can accomplish the goal that generating an image with bounding box label.
