Object Detection System Using Yolo Algorithm

Authors

  • R.A. Arul Raja  Assistant Professor (Sr. G), Department of Mechanical Engineering, SRM Institute of Science and Technology, Vadapalani campus, Chennai, Tamil Nadu, India. Email: raarulraja@gmail.com
  • V.G. Anisha Gnana Vincy  Assistant professor, Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Padappai, Chennai, Tamil Nadu, India.

Keywords:

Object Detection, Deep Learning, Fog computing, Cloud computing, Internet of Things

Abstract

In the most recent ten years, there has been a tremendous amount of study in the field of computer vision due to the pervasive and broad applications such scene interpretation, video surveillance, robotics, and self-driving systems. Visual recognition systems, which include picture categorization, localization, and detection, have gained significant research momentum as the foundation of all these applications. These visual recognition algorithms have achieved exceptional performance as a result of substantial advancements in neural networks, particularly deep learning. One of these areas where computer vision has had considerable success is object detection. This research clarifies the function of convolutional neural network-based deep learning algorithms for object detection. A list of deep learning frameworks and object detection services is also provided. This article introduces readers to the YOLO object identification technique and explains how it works.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2022-09-30

Issue

Volume 9, Issue 5, September-October-2022

Section

Research Articles

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Copyright (c) IJSRSET

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
R.A. Arul Raja, V.G. Anisha Gnana Vincy "Object Detection System Using Yolo Algorithm" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 5, pp.295-300, September-October-2022.