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

EasyChair Preprint no. 8496

8 pagesDate: July 17, 2022


The field of artificial intelligence is built on object detection techniques. YOU ONLY LOOK ONCE (YOLO) algorithm and its 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 applications, etc. This article reached a number 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 describes the development process of the YOLO algorithm.

Keyphrases: CNN, object detection, YOLO

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {V Viswanatha and R K Chandana and A.C Ramachandra},
  title = {Real Time Object Detection System with YOLO and CNN Models: a Review},
  howpublished = {EasyChair Preprint no. 8496},

  year = {EasyChair, 2022}}
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