Cloud-Based Image Processing Using Deep Learning for Real-Time Object Detection Review

Abstract

The growing demand for real-time image processing in various industries has led to the integration of cloud-based platforms with deep learning technologies. This paper proposes a cloud-based framework that utilizes deep learning models for real-time object detection, offering a scalable and efficient solution for high-performance image analysis. The proposed system leverages cloud infrastructure for enhanced computational power and storage, while utilizing convolutional neural networks (CNNs) to improve object detection accuracy. The architecture allows for real-time processing, enabling applications in fields such as autonomous vehicles, surveillance, and industrial automation. Experimental results demonstrate the system’s ability to detect multiple objects with high accuracy and low latency. This approach significantly reduces the hardware limitations traditionally associated with on-premise solutions, making it highly applicable to large-scale deployments. The paper also explores potential challenges related to latency, data privacy, and bandwidth requirements, proposing solutions for these issues.