An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector

TL;DR

This paper introduces a fast, layout-independent ALPR system based on YOLO that achieves high accuracy across multiple datasets and is robust under various conditions, with publicly available annotations.

Contribution

The paper develops a unified YOLO-based ALPR system with optimized models for speed and accuracy, and provides a large, publicly available dataset annotation resource.

Findings

Achieved 96.9% end-to-end recognition rate across eight datasets.

Operates in real-time with high FPS on high-end GPUs.

Outperformed previous methods and commercial systems in several datasets.

Abstract

This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The system is conceived by evaluating and optimizing different models, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.9% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community.