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

• URL: http://arxiv.org/abs/2208.00773v1
• Date: Sat, 23 Jul 2022 11:00:11 GMT
• Title: Real Time Object Detection System with YOLO and CNN Models: A Review
• Authors: Viswanatha V, Chandana R K, Ramachandra A.C.
• Abstract summary: 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.
• Score: 7.767212366020168
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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