CLOUDVISION: AI-Driven Image Recognition Platform

Abstract

Image recognition has evolved rapidly from rule-based systems to deep learning models, driven by the exponential growth in visual data and computing power. Traditional on-premise solutions struggle to meet the demands of large-scale, real-time image processing due to limitations in scalability, cost, and operational efficiency. This research addresses the challenge of building a scalable and cost-effective AI image recognition system by leveraging cloud infrastructure. The proposed method integrates deep learning-based image classification with Amazon Web Services (AWS), utilizing EC2 for computation, SQS for asynchronous task handling, and S3 for persistent storage within a modular, auto-scalable architecture. The system demonstrates high throughput, elastic resource management, and reliable classification accuracy across dynamic workloads. Results confirm enhanced performance, cost efficiency, and fault tolerance, making it a viable solution for diverse industries such as healthcare, security, and smart surveillance.

Introduction

The rapid increase in visual data from sources like smartphones, surveillance, social media, and healthcare has driven a need for automated, accurate, and scalable image recognition systems. Traditional rule-based methods have been replaced by deep learning, especially convolutional neural networks (CNNs), known for high accuracy but requiring significant computational resources often beyond on-premise capabilities.

Industries such as healthcare, security, and transportation demand image classification systems that not only deliver high accuracy but also scale efficiently and respond quickly to variable workloads. Cloud computing offers a flexible, scalable, and cost-effective solution for deploying such AI models.

This study introduces CLOUDVISION, a cloud-native, AI-powered image recognition platform built on Amazon Web Services (AWS). CLOUDVISION uses EC2 instances for computation, SQS for task queuing, and S3 for result storage within a modular microservices architecture. The system supports auto-scaling up to 19 EC2 instances to handle varying loads and combines a Java Spring Boot backend with Python-based deep learning inference.

CLOUDVISION leverages pre-trained models like ResNet and Xception, fine-tuned via transfer learning on the large-scale ImageNet dataset, ensuring high classification accuracy and adaptability. Users submit image URLs through a web interface, which are processed asynchronously through the system, culminating in real-time classification results returned to the user.

Performance evaluation shows the Xception model achieves 94.1% accuracy, slightly better than ResNet’s 92.5%, validating the platform’s effectiveness. Latency analysis reveals low overhead from the web and queuing layers, with the majority of processing time spent on model inference, highlighting optimization opportunities.

Overall, CLOUDVISION demonstrates a reliable, scalable, and efficient cloud-based framework for real-time and batch image recognition applicable across multiple domains.

Conclusion

The CLOUDVISION platform effectively solves key challenges in cloud-based image recognition, including scalability, cost-efficiency, and real-time processing. Built with a modular, cloud-native architecture using AWS services like EC2, SQS, and S3, it ensures efficient task distribution, flexible scaling, and reliable storage of output data. The core AI engine uses deep learning models such as ResNet and Xception, achieving high accuracy and fast response times—even under variable workloads. Features like message queuing and auto-scaling optimize performance and resource usage, making the system cost-effective and suitable for enterprise deployment. Performance evaluations show strong results, with low latency, high accuracy, and lower operational costs compared to traditional setups. The system’s layered design—including a user interface, secure APIs, asynchronous processing, and GPU-powered inference—ensures modularity, reliability, and easy adaptation across industries such as healthcare, security, and content moderation. Future upgrades may include video stream analysis, edge computing for IoT devices, and advanced models like YOLO and Vision Transformers. Enhancements like AI-based workload prediction and multi-cloud support will further improve scalability, performance, and cost control. With these developments, CLOUDVISION aims to become a comprehensive AI-powered platform for visual recognition tasks over numerous applications.

References

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Copyright

Copyright © 2025 Ms. Archana K, Mrs. Jennifer Mary S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.