A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition

Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats...

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Autores principales:	Ahmed Abdelmoamen Ahmed, Sheikh Ahmed
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	real-time image processing ANPR machine learning mobile computing Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/09259924aa034206aff4e9791d02b418
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spelling	oai:doaj.org-article:09259924aa034206aff4e9791d02b4182021-11-25T16:13:07ZA Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition10.3390/a141103171999-4893https://doaj.org/article/09259924aa034206aff4e9791d02b4182021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/317https://doaj.org/toc/1999-4893Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate’s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters’ contours within the grayscale image. Third, the detected the alphanumeric characters’ contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.Ahmed Abdelmoamen AhmedSheikh AhmedMDPI AGarticlereal-timeimage processingANPRmachine learningmobile computingIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 317, p 317 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	real-time image processing ANPR machine learning mobile computing Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	real-time image processing ANPR machine learning mobile computing Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Ahmed Abdelmoamen Ahmed Sheikh Ahmed A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
description	Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate’s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters’ contours within the grayscale image. Third, the detected the alphanumeric characters’ contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.
format	article
author	Ahmed Abdelmoamen Ahmed Sheikh Ahmed
author_facet	Ahmed Abdelmoamen Ahmed Sheikh Ahmed
author_sort	Ahmed Abdelmoamen Ahmed
title	A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
title_short	A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
title_full	A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
title_fullStr	A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
title_full_unstemmed	A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
title_sort	real-time car towing management system using ml-powered automatic number plate recognition
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/09259924aa034206aff4e9791d02b418
work_keys_str_mv	AT ahmedabdelmoamenahmed arealtimecartowingmanagementsystemusingmlpoweredautomaticnumberplaterecognition AT sheikhahmed arealtimecartowingmanagementsystemusingmlpoweredautomaticnumberplaterecognition AT ahmedabdelmoamenahmed realtimecartowingmanagementsystemusingmlpoweredautomaticnumberplaterecognition AT sheikhahmed realtimecartowingmanagementsystemusingmlpoweredautomaticnumberplaterecognition
_version_	1718413238653157376

A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition

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