Literature Survey on YOLO Models for Face Recognition in Covid-19 Pandemic

https://doi.org/10.55529/jipirs.34.27.35

Authors

  • Aseil Nadhum Kadhum Imam Al-Kadhim College, Iraq.
  • Aseel Nadhum Kadhum Imam Al-Kadhim College, Iraq.

Keywords:

YOLO, Object Detection, Bounding Boxes, Neural Network, YOLO V1, YOLO V2, YOLO V3.

Abstract

Artificial Intelligence and robotics the fields in which there is necessary required object detection algorithms. In this study, YOLO and different versions of YOLO are studied to find out advantages of each model as well as limitations of each model. Even in this study, YOLO version similarities and differences are studied. Improvement in the YOLO (You Only Look Once) as well as CNN (Convolutional Neural Network) is the research study present going on for different object detection. In this paper, each YOLO version model is discussed in detail with advantages, limitations and performance. YOLO updated versions such as YOLO v1, YOLO v2, YOLO v3, YOLO v4, YOLO v5 and YOLO v7 are studied and showed superior performance of YOLO v7 over other versions of YOLO algorithm.

Published

2023-07-29

How to Cite

Aseil Nadhum Kadhum, & Aseel Nadhum Kadhum. (2023). Literature Survey on YOLO Models for Face Recognition in Covid-19 Pandemic. Journal of Image Processing and Intelligent Remote Sensing, 3(04), 27–35. https://doi.org/10.55529/jipirs.34.27.35

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