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

TL;DR

This survey reviews YOLO and CNN models for real-time object detection, highlighting their effectiveness, development, and applications across various industries, emphasizing their ability to improve detection accuracy and efficiency.

Contribution

It provides a comprehensive overview of YOLO and CNN architectures, their development, and applications in real-time object detection, offering insights and perspectives for future research.

Findings

YOLO effectively generalizes object representation without significant precision loss.

CNN models can eliminate highlights and identify objects in complex images.

The survey highlights applications in finance, education, and medical diagnosis.

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.