Automatic License Plate Recognition in Real-World Traffic Videos Captured in Unconstrained Environment by a Mobile Camera

Automatic License Plate Recognition (ALPR) has remained an active research topic for years due to various applications, especially in Intelligent Transportation Systems (ITS). This paper presents an efficient ALPR technique based on deep learning, which accurately performs license plate (LP) recogni...

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Veröffentlicht in:Electronics (Basel) 2022-05, Vol.11 (9), p.1408
Hauptverfasser: Khan, Ishtiaq Rasool, Ali, Syed Talha Abid, Siddiq, Asif, Khan, Muhammad Murtaza, Ilyas, Muhammad Usman, Alshomrani, Saleh, Rahardja, Susanto
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container_issue 9
container_start_page 1408
container_title Electronics (Basel)
container_volume 11
creator Khan, Ishtiaq Rasool
Ali, Syed Talha Abid
Siddiq, Asif
Khan, Muhammad Murtaza
Ilyas, Muhammad Usman
Alshomrani, Saleh
Rahardja, Susanto
description Automatic License Plate Recognition (ALPR) has remained an active research topic for years due to various applications, especially in Intelligent Transportation Systems (ITS). This paper presents an efficient ALPR technique based on deep learning, which accurately performs license plate (LP) recognition tasks in an unconstrained environment, even when trained on a limited dataset. We capture real traffic videos in the city and label the LPs and the alphanumeric characters in the LPs within different frames to generate training and testing datasets. Data augmentation techniques are applied to increase the number of training and testing samples. We apply the transfer learning approach to train the recently released YOLOv5 object detecting framework to detect the LPs and the alphanumerics. Next, we train a convolutional neural network (CNN) to recognize the detected alphanumerics. The proposed technique achieved a recognition rate of 92.8% on a challenging proprietary dataset collected in several jurisdictions of Saudi Arabia. This accuracy is higher than what was achieved on the same dataset by commercially available Sighthound (86%), PlateRecognizer (67%), OpenALPR (77%), and a state-of-the-art recent CNN model (82%). The proposed system also outperformed the existing ALPR solutions on several benchmark datasets.
doi_str_mv 10.3390/electronics11091408
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source MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals
subjects Access control
Accuracy
Algorithms
Artificial neural networks
Automatic vehicle identification systems
Cameras
Datasets
Deep learning
Intelligent transportation systems
License plates
Licenses
Morphology
Neural networks
Recognition
Sensors
Traffic
Training
Video
title Automatic License Plate Recognition in Real-World Traffic Videos Captured in Unconstrained Environment by a Mobile Camera
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