Object Detection (OD) is one of the most critical tasks in 2D image processing. The researchers proposed multiple math models and frameworks based on Deep Convolutional Networks, such as R-CNN, SSD, and YOLO are the most common. Generative Adversarial Nets (GAN) represent a prominent field of study in machine learning, and it has been applied to many tasks with exciting results. This work aims to assess the potential of GANs applied to OD tasks and the proposed frameworks as a field of study. The methodology used was a systemic review of 14 papers. The conclusion shows that although OD and GANs are popular themes, there are not many developments in the intersection of both subjects. Therefore, OD with GAN-applied tasks is an excellent field to explore in future works.

Object Detection (OD) is one of the most important tasks in 2D image processing. Multiple math models have been proposed and frameworks based in Deep Convolutional Networks such as R-CNN, SSD and YOLO are most common. Generative Adversarial Nets (GAN's) represent a prominent field of study in machine learning and it has been applied to many tasks with exciting results. The objective of this work is to assess the potential of GAN's applied to OD tasks and the proposed frameworks as field of study. The methodology used was a systemically review of 14 papers. The conclusion shows that even though OD and GAN's are popular themes, there are not many developments done in the intersection of both subjects. Therefore, OD with GAN applied tasks are an excellent field to explore in future works.