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.

翻译：在数据集小的时候,数据扩增一直是克服超配问题的有效办法。 已经有许多扩增操作, 如水平翻转、随机裁剪甚至混合等。 然而, 与图像分类任务不同, 我们无法简单地执行这些用于目标探测任务的操作, 原因是缺少贴有标签的捆绑框信息, 用于相应的生成图像。 为了应对这一挑战, 我们提议了一个框架, 利用基因反转网络( GAN) 来进行不受监督的数据扩增 。 要具体化, 我们基于最近YOLOv4 的最高性能, 我们提议了一条两步管道, 使我们能够生成一个图像, 显示对象处于某种位置。 这样, 我们就可以实现一个目标, 即通过捆绑框标签生成图像 。