Real Time Object Detection System with YOLO and CNN Models: A Review

Viswanatha V, Chandana R K, Ramachandra A. C.
7/23/2022
cs.CVeess.IV

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK ONCE (YOLO) algorithm and it's more evolved versions are briefly described in this research survey. This survey is all about YOLO and convolution neural networks (CNN)in the direction of real time object detection.YOLO does generalized object representation more effectively without precision losses than other object detection models.CNN architecture models have the ability to eliminate highlights and identify objects in any given image. When implemented appropriately, CNN models can address issues like deformity diagnosis, creating educational or instructive application, etc. This article reached atnumber of observations and perspective findings through the analysis.Also it provides support for the focused visual information and feature extraction in the financial and other industries, highlights the method of target detection and feature selection, and briefly describe the development process of YOLO algorithm.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{v2022real,
  title  = {Real Time Object Detection System with YOLO and CNN Models: A Review},
  author = {Viswanatha V and Chandana R K and Ramachandra A. C.},
  year   = {2022},
  eprint = {2208.00773},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2208.00773v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2208.00773.svg)](https://codeofpaper.com/paper/2208.00773)

Discussion