Vehicle Number Plate Detection Using Deep Learning Technique

Abstract

This paper introduces an Automatic License Plate Detection (ALPD) system, incorporating You Only Look Once version 8 (YOLO v8) for real-time object detection and OpenCV for efficient image processing. With a focus on achieving accurate and swift license plate recognition, the system employs YOLO v8 for precise localization within images or video streams, complemented by OpenCV for effective preprocessing tasks. The Optical Character Recognition (OCR) component of the system adopts a collaborative approach, leveraging Easy OCR. This ensemble enhances the system's proficiency in accurately extracting alphanumeric characters from license plates, even in challenging conditions like variable lighting and complex backgrounds. Beyond license plate identification, the system emphasizes the extraction of relevant textual information, making it versatile for applications in law enforcement, traffic management, and security. Through The model were evaluated based on their accuracy, precision recall. YOLOv8 highest accuracy 97%. This research highlights the important of integrating advance deep learning technique. detailed implementation insights and experimental results, this paper showcases the effectiveness of combining YOLO v8 and OpenCV for object detection, as well as the collaborative strength of Easy OCR for precise character recognition. The experimental results underscore the system's high accuracy and realtime performance, positioning it as a reliable tool for intelligent transportation systems and security applications. This ALPD system offers a robust solution for diverse use cases, contributing to advancements in efficient license plate recognition technology