Next-Generation License Plate Detection and Recognition System using YOLOv8

TL;DR

This paper presents an optimized YOLOv8-based system for license plate detection and recognition, achieving high accuracy and efficiency suitable for real-time deployment in intelligent transportation systems.

Contribution

It introduces a novel pipeline combining YOLOv8 Nano and Small variants with a character sequencing method for improved license plate recognition.

Findings

YOLOv8 Nano achieved 0.964 precision on LPR

YOLOv8 Small achieved 0.92 precision on Character Recognition

The system maintains high accuracy with computational efficiency

Abstract

In the evolving landscape of traffic management and vehicle surveillance, efficient license plate detection and recognition are indispensable. Historically, many methodologies have tackled this challenge, but consistent real-time accuracy, especially in diverse environments, remains elusive. This study examines the performance of YOLOv8 variants on License Plate Recognition (LPR) and Character Recognition tasks, crucial for advancing Intelligent Transportation Systems. Two distinct datasets were employed for training and evaluation, yielding notable findings. The YOLOv8 Nano variant demonstrated a precision of 0.964 and mAP50 of 0.918 on the LPR task, while the YOLOv8 Small variant exhibited a precision of 0.92 and mAP50 of 0.91 on the Character Recognition task. A custom method for character sequencing was introduced, effectively sequencing the detected characters based on their x-axis positions. An optimized pipeline, utilizing YOLOv8 Nano for LPR and YOLOv8 Small for Character Recognition, is proposed. This configuration not only maintains computational efficiency but also ensures high accuracy, establishing a robust foundation for future real-world deployments on edge devices within Intelligent Transportation Systems. This effort marks a significant stride towards the development of smarter and more efficient urban infrastructures.