Next-Generation License Plate Detection and Recognition System Using YOLOv8

2023-12-04

International Symposium on High-capacity Optical Networks and Enabling Technologies

AI Summary

To address the joint bottlenecks of accuracy, real-time performance, and edge deployability in license plate detection and recognition (LPR) systems under complex traffic scenarios, this paper proposes a lightweight two-stage YOLOv8 collaborative framework: YOLOv8n for efficient license plate localization and YOLOv8s for character recognition. We introduce a novel RNN-free and attention-free character serialization algorithm based on X-axis coordinates. Integrated with multi-scale data augmentation and edge-optimized inference, the system achieves Precision = 0.964 and mAP₅₀ = 0.918 for plate detection, and Precision = 0.92 and mAP₅₀ = 0.91 for character recognition—outperforming existing lightweight LPR methods. Crucially, it supports real-time inference on edge devices. Our core contributions are: (1) a dual-model collaborative architecture tailored for LPR, and (2) a lightweight, geometry-driven character ordering paradigm that eliminates sequential modeling overhead.

Technology Category

Machine Learning: Learning on the Edge & Model CompressionComputer Vision: Learning & Optimization for CVPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Develops a license plate detection and recognition system for traffic management.
• Evaluates YOLOv8 models for real-time accuracy in diverse environments.
• Proposes an optimized pipeline for edge devices in transportation systems.

Innovation

• Uses YOLOv8 Nano for license plate detection
• Uses YOLOv8 Small for character recognition
• Introduces custom method for character sequencing