ADVANCED AI-DRIVEN SYSTEM FOR VECHICLE CLASSIFICATION AND AUTOMATED NUMBER PLATE RECOGNITION

Abstract

This paper presents an advanced AI-driven system designed for vehicle classification and automated number plate recognition (ANPR). Leveraging state-of-the-art machine learning algorithms, the system offers robust performance in diverse environmental conditions. The core components include convolutional neural networks (CNNs) for image processing and optical character recognition (OCR) for number plate detection. The proposed solution demonstrates high accuracy and speed, making it suitable for real-time applications in traffic management and law enforcement. This innovative approach not only improves the efficiency and accuracy of vehicle identification but also reduces the need for manual intervention, paving the way for smarter, more autonomous traffic systems.

The AI-driven system is built on a scalable architecture that can be easily integrated into existing traffic management infrastructures. This flexibility ensures that the system can be adapted to various urban and rural settings, addressing the unique challenges posed by different environments. The incorporation of machine learning models enables continuous improvement of the system’s performance over time, as it learns from new data and adapts to changing conditions. The paper discusses the technical details of the system, including the data preprocessing, model training, and deployment processes.