Volume 2, Issue 1, 2022
Articles

Object Detection Using YOLO Algorithm

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  • Anugrah C. Biju,
  • Amal K. George,
  • Vignesh K. H.
Anugrah C. Biju
Department of Computer Science [PG], Kristu Jayanti College, Bengaluru
Amal K. George
Department of Computer Science [PG], Kristu Jayanti College, Bengaluru
Vignesh K. H.
Department of Computer Science [PG], Kristu Jayanti College, Bengaluru
DOI: https://doi.org/10.59176/kjcs.v2i1.2219

Published 2022-06-08

Keywords

  • Object Detection, YOLO Algorithm, YOLO Versions, Neural Network.

How to Cite

Biju, A. C., George, A. K., & K. H., V. . (2022). Object Detection Using YOLO Algorithm. Kristu Jayanti Journal of Computational Sciences (KJCS), 2(1), 25–37. https://doi.org/10.59176/kjcs.v2i1.2219
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Abstract

The aim of this study is to find a stable system that can detect the object in fraction of seconds from different sources like image, surveillances, bus, car etc. There are different types of algorithms to find the object with the help of frame. This study is to recognize the object classification and localization. The trained weights should have the maximum confident level possible when classifying objects from external events or detecting multiple objects from an image.

To detect the object in the existing system, we use to split the image into different class and focus on the specific region or the subject in the frame. Our proposal using the YOLO algorithm model detects and recognizes the objects, and the improved model will examine the entire image. The YOLO model splits the image into regions and maps the confidence probability using a neural network on the image.

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