A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry

Automatic License Plate Recognition (ALPR) is a widely used technology. However, due to the influence of complex environmental factors, recognition accuracy and speed of license plate recognition have been challenged and expected. Aiming to construct a sufficiently robust license plate recognition m...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Chun-Liang Tung, Ching-Hsin Wang, Bo-Syuan Peng
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/a481aadcb4784289866dbeb88374b5d5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a481aadcb4784289866dbeb88374b5d5
record_format	dspace
spelling	oai:doaj.org-article:a481aadcb4784289866dbeb88374b5d52021-11-22T01:10:42ZA Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry1563-514710.1155/2021/3723715https://doaj.org/article/a481aadcb4784289866dbeb88374b5d52021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3723715https://doaj.org/toc/1563-5147Automatic License Plate Recognition (ALPR) is a widely used technology. However, due to the influence of complex environmental factors, recognition accuracy and speed of license plate recognition have been challenged and expected. Aiming to construct a sufficiently robust license plate recognition model, this study adopted multitask learning in the license plate detection stage, used the convolutional neural networks of single-stage detection, RetinaFace, and MobileNet, as approaches to license plate location, and completed the license plate sampling through the calculation of license plate skew correction. In the license plate character recognition stage, the Convolutional Recurrent Neural Network (CRNN) integrated with the loss function of the CTC model was employed as a segmentation-free and highly robust method of license plate character recognition. In this study, after the license plate recognition model, DLPR, trained the PVLP dataset of vehicle images provided by company A in Taiwan’s data processing industry, it performed tests on the PVLP dataset, indicating that its precision was 98.60%, recognition accuracy was 97.56%, and recognition speed was FPS > 21. In addition, according to the tests on the public AOLP dataset of Taiwan’s vehicles, its recognition accuracy was 97.70% and recognition speed was FPS > 62. Therefore, not only can the DLPR model be applied to the license plate recognition of real-time image streams in the future, but also it can assist the data processing industry in enhancing the accuracy of license plate recognition in photos of traffic violations and the performance of traffic service operations.Chun-Liang TungChing-Hsin WangBo-Syuan PengHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Chun-Liang Tung Ching-Hsin Wang Bo-Syuan Peng A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry
description	Automatic License Plate Recognition (ALPR) is a widely used technology. However, due to the influence of complex environmental factors, recognition accuracy and speed of license plate recognition have been challenged and expected. Aiming to construct a sufficiently robust license plate recognition model, this study adopted multitask learning in the license plate detection stage, used the convolutional neural networks of single-stage detection, RetinaFace, and MobileNet, as approaches to license plate location, and completed the license plate sampling through the calculation of license plate skew correction. In the license plate character recognition stage, the Convolutional Recurrent Neural Network (CRNN) integrated with the loss function of the CTC model was employed as a segmentation-free and highly robust method of license plate character recognition. In this study, after the license plate recognition model, DLPR, trained the PVLP dataset of vehicle images provided by company A in Taiwan’s data processing industry, it performed tests on the PVLP dataset, indicating that its precision was 98.60%, recognition accuracy was 97.56%, and recognition speed was FPS > 21. In addition, according to the tests on the public AOLP dataset of Taiwan’s vehicles, its recognition accuracy was 97.70% and recognition speed was FPS > 62. Therefore, not only can the DLPR model be applied to the license plate recognition of real-time image streams in the future, but also it can assist the data processing industry in enhancing the accuracy of license plate recognition in photos of traffic violations and the performance of traffic service operations.
format	article
author	Chun-Liang Tung Ching-Hsin Wang Bo-Syuan Peng
author_facet	Chun-Liang Tung Ching-Hsin Wang Bo-Syuan Peng
author_sort	Chun-Liang Tung
title	A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry
title_short	A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry
title_full	A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry
title_fullStr	A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry
title_full_unstemmed	A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry
title_sort	deep learning model of dual-stage license plate recognition applicable to the data processing industry
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/a481aadcb4784289866dbeb88374b5d5
work_keys_str_mv	AT chunliangtung adeeplearningmodelofdualstagelicenseplaterecognitionapplicabletothedataprocessingindustry AT chinghsinwang adeeplearningmodelofdualstagelicenseplaterecognitionapplicabletothedataprocessingindustry AT bosyuanpeng adeeplearningmodelofdualstagelicenseplaterecognitionapplicabletothedataprocessingindustry AT chunliangtung deeplearningmodelofdualstagelicenseplaterecognitionapplicabletothedataprocessingindustry AT chinghsinwang deeplearningmodelofdualstagelicenseplaterecognitionapplicabletothedataprocessingindustry AT bosyuanpeng deeplearningmodelofdualstagelicenseplaterecognitionapplicabletothedataprocessingindustry
_version_	1718418360146853888

A Deep Learning Model of Dual-Stage License Plate Recognition Applicable to the Data Processing Industry

Ejemplares similares