2D Image Object Detection Aided by Generative Adversarial Networks: A Literature Review

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...

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Bibliographic Details
Published inJOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH Vol. 5; no. 3; pp. 202 - 207
Main Authors Caio Vinicius Bertolini, Roberto Monteiro
Format Journal Article
LanguageEnglish
Published 15.11.2022
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Summary: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.
ISSN:2764-5886
2764-622X
DOI:10.34178/jbth.v5i3.228