A Study on AI Deep Learning Approaches for Real-Time Object Detection and Recognition

Abstract

This study explores various AI deep learning approaches for real-time object detection and recognition, a critical area in computer vision with applications ranging from autonomous vehicles to surveillance systems. The paper delves into state-of-the-art algorithms such as Convolution Neural Networks (CNNs), Region-Based CNNs (R-CNNs), Single Shot Multi Box Detectors (SSD), and You Only Look Once (YOLO) frameworks, evaluating their performance in terms of accuracy, speed, and computational efficiency. Additionally, the research highlights key challenges such as scalability, environmental variability, and the trade-offs between model complexity and real-time processing capabilities. Experimental results demonstrate the effectiveness of integrating deep learning techniques for dynamic, real-world environments, suggesting potential optimizations for future advancements in object detection and recognition technologies. The findings offer insights into the practical deployment of AI systems in time-sensitive applications, contributing to the ongoing development of efficient and robust detection mechanisms.