Automatic License Plate Detection Using YOLOv8 Model

This paper presents an advanced Automatic License Plate Recognition (ALPR) system designed for efficient traffic violation monitoring and electronic ticketing. Leveraging the You Only Look Once (YOLO) object detection frame work using its latest model YOLOv8 and EasyOCR for optical character recognition, the system accurately detects and recognizes vehicle license plates within image frames. It has been developed using Python and OpenCV for image processing to improve the performance of the system. Vehicle detection and license plate localization trigger the ALPR pipeline with YOLOv8. Images of the detected plates are further refined in OpenCV to prepare them for character recognition. EasyOCR reads and extracts characters from the license plate. The following system shows the correct detection of license plates with 100 percentage of accuracy and character recognition with a rate of 94 percentage on a dataset of ten images. The system proposed is a robust, scalable, and cost-effective solution to demonstrate promising potential for traffic management applications. In this respect, the proposed ALPR system can be another practical and cost-effective option, in which governance and control of traffic have a huge say. To today’s date, a partial success is being witnessed with the system, which itself brings the justification to introduce advanced object detection and OCR technologies into the construction of an intelligent traffic surveillance system.