Automatic Number Plate Detection with Image Recognition System

Abstract

ANPR systems are very important fo traffic system safe and under control.To make these systems work well, you need to make models that are both quick and correct.This study examines the application of YOLOv8, a contemporary object detection model for the analysis of license plates in Bangladesh.We took pictures of cars in a lot of different places and styles.The dataset taught five different versions of the YOLOv8 model: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x.We are used standard metrics like recall, F1 score, and mean Average Precision (mAP) to find out how well the models worked.The result are showed that YOLOv8 could read license plates from Bangladesh correctly.The results show that YOLOv8 can deal with the special things about Bangladeshi plates.The study also helps you pick a model based on how fast and powerful you need your computer to be.We will add these models to a complete ANPR system and improve the dataset in the future to make traffic management and safety better