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

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.

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Cited by in corpus (21)

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• Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
• Image-based Automatic Dial Meter Reading in Unconstrained Scenarios
• Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers
• A First Look at Dataset Bias in License Plate Recognition
• Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution
• Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition
• Leveraging Model Fusion for Improved License Plate Recognition
• Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark
• Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach
• Character Time-series Matching For Robust License Plate Recognition
• Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation
• Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation
• LPLC: A Dataset for License Plate Legibility Classification
• SuperOCR: A Conversion from Optical Character Recognition to Image Captioning
• An improved helmet detection method for YOLOv3 on an unbalanced dataset
• Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning
• Relaxed syntax modeling in Transformers for future-proof license plate recognition
• Real-time smart vehicle surveillance system