TL;DR
This paper introduces a robust real-time ALPR system based on YOLO, achieving high accuracy and speed across diverse datasets and outperforming commercial solutions in challenging real-world scenarios.
Contribution
The paper presents a novel YOLO-based ALPR system with specialized CNN training and data augmentation, improving robustness and accuracy in real-world conditions.
Findings
Achieved 93.53% recognition rate on SSIG dataset
Attained 78.33% recognition rate on UFPR-ALPR dataset
Operates at 47 FPS and 35 FPS on different datasets
Abstract
Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. The Convolutional Neural Networks (CNNs) are trained and fine-tuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system…
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Taxonomy
MethodsCR-NET · Fast-YOLOv2 · YOLOv2
