Real Time Object Detection System by using YOLO Framework

Author(s)	Awadhesh Singh Chouhan, Shivam Rathore, Roshan Dhomane, Girdhar Kalamdhad, Prof. Nilesh Mishra
Country	India
Abstract	Real-time object detection has emerged as a crucial task in computer vision, with applications spanning autonomous vehicles, surveillance, and robotics. This paper provides a comprehensive analysis of state-of-the-art object detection models, particularly focusing on YOLO variants (YOLOv3, YOLOv7) and their comparative performance. Various training methodologies, including step-by-step training, Faster R-CNN, and hybrid deep learning models, are explored to highlight advancements in accuracy and computational efficiency[1]. Experimental results demonstrate that YOLOv7 surpasses YOLOv3 in accuracy, while achieves state-of-the-art performance without additional pretraining datasets. This study contributes to the ongoing development of real-time detection systems by analyzing the trade-offs between speed, precision, and practical implementation.
Keywords	Real-Time Object Detection, YOLO, Faster R-CNN, Deep Learning
Field	Engineering
Published In	Volume 6, Issue 3, March 2025
Published On	2025-03-18
Cite This	Real Time Object Detection System by using YOLO Framework - Awadhesh Singh Chouhan, Shivam Rathore, Roshan Dhomane, Girdhar Kalamdhad, Prof. Nilesh Mishra - IJLRP Volume 6, Issue 3, March 2025.

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