AI Based Real Time Object Detection and Analysis of Multiple Object Existence

Abstract

Accurate and efficient object detection and counting are crucial in modern computer vision, especially for real-time applications. However, existing methods often struggle with challenges like processing delays, low accuracy in complex environments, and difficulty in detecting overlapping objects. To address these drawbacks, this project proposes an AI-based solution using the YOLO (You Only Look Once) framework, integrated with OpenCV for enhanced image processing. Our system processes video streams as input, performing real-time object detection and counting with high accuracy. It ensures reliable performance under challenging conditions, such as low lighting, cluttered environments, and partial occlusion. OpenCV optimizes input data through noise reduction and image sharpening, improving detection precision.