YOLOv5 for Automatic License Plate Recognition in Smart Cities

Abir Raza; Elarbi Badidi; Basma Badidi; Sarah Al Zahmi

doi:10.1007/978-3-031-42317-8_8

YOLOv5 for Automatic License Plate Recognition in Smart Cities

Abir Raza
, Elarbi Badidi
, Basma Badidi
, Sarah Al Zahmi

Department of Computer Science and Software Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Recognition and extraction of license plate information from still images or videos are the basis of modern traffic and security systems. Automatic License Plate Recognition (ALPR) is transforming public safety and transportation in many ways. Such license plate recognition systems enable advanced solutions for toll roads, offer significant operational cost savings through automation, and even open up new market opportunities, such as license plate readers mounted on police vehicles. In this work, we implement license plate recognition based on convolutional neural networks to achieve high accuracy. You Only Look Once (YOLO) algorithm is employed for detection, while, an Optical Character Recognition (OCR) technique is used for text recognition. We use four variants of the YOLOv5 for license plate detection and the EasyOCR for license plate recognition. The results show that YOLOv5x (extra-large) achieves a Mean Average Precision (mAP) of nearly 82% in detecting vehicles and license plates in an image. Furthermore, the recognition of the letters on license plates is achieved with a confidence score of nearly 65%.

Original language	English
Title of host publication	The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -
Editors	Muhammad Younas, Irfan Awan, Salima Benbernou, Dana Petcu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	93-105
Number of pages	13
ISBN (Print)	9783031423161
DOIs	https://doi.org/10.1007/978-3-031-42317-8_8
Publication status	Published - 2023
Event	4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023 - Marrakech, Morocco Duration: Aug 14 2023 → Aug 16 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	768 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023
Country/Territory	Morocco
City	Marrakech
Period	8/14/23 → 8/16/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Convolutional neural networks
Deep learning
Object detection
Object recognition

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-3-031-42317-8_8

Cite this

Raza, A., Badidi, E., Badidi, B., & Al Zahmi, S. (2023). YOLOv5 for Automatic License Plate Recognition in Smart Cities. In M. Younas, I. Awan, S. Benbernou, & D. Petcu (Eds.), The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) - (pp. 93-105). (Lecture Notes in Networks and Systems; Vol. 768 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-42317-8_8

YOLOv5 for Automatic License Plate Recognition in Smart Cities. / Raza, Abir; Badidi, Elarbi; Badidi, Basma et al.
The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -. ed. / Muhammad Younas; Irfan Awan; Salima Benbernou; Dana Petcu. Springer Science and Business Media Deutschland GmbH, 2023. p. 93-105 (Lecture Notes in Networks and Systems; Vol. 768 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Raza, A, Badidi, E, Badidi, B & Al Zahmi, S 2023, YOLOv5 for Automatic License Plate Recognition in Smart Cities. in M Younas, I Awan, S Benbernou & D Petcu (eds), The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -. Lecture Notes in Networks and Systems, vol. 768 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 93-105, 4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023, Marrakech, Morocco, 8/14/23. https://doi.org/10.1007/978-3-031-42317-8_8

Raza A, Badidi E, Badidi B, Al Zahmi S. YOLOv5 for Automatic License Plate Recognition in Smart Cities. In Younas M, Awan I, Benbernou S, Petcu D, editors, The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -. Springer Science and Business Media Deutschland GmbH. 2023. p. 93-105. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-42317-8_8

Raza, Abir ; Badidi, Elarbi ; Badidi, Basma et al. / YOLOv5 for Automatic License Plate Recognition in Smart Cities. The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023) -. editor / Muhammad Younas ; Irfan Awan ; Salima Benbernou ; Dana Petcu. Springer Science and Business Media Deutschland GmbH, 2023. pp. 93-105 (Lecture Notes in Networks and Systems).

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abstract = "Recognition and extraction of license plate information from still images or videos are the basis of modern traffic and security systems. Automatic License Plate Recognition (ALPR) is transforming public safety and transportation in many ways. Such license plate recognition systems enable advanced solutions for toll roads, offer significant operational cost savings through automation, and even open up new market opportunities, such as license plate readers mounted on police vehicles. In this work, we implement license plate recognition based on convolutional neural networks to achieve high accuracy. You Only Look Once (YOLO) algorithm is employed for detection, while, an Optical Character Recognition (OCR) technique is used for text recognition. We use four variants of the YOLOv5 for license plate detection and the EasyOCR for license plate recognition. The results show that YOLOv5x (extra-large) achieves a Mean Average Precision (mAP) of nearly 82\% in detecting vehicles and license plates in an image. Furthermore, the recognition of the letters on license plates is achieved with a confidence score of nearly 65\%.",

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author = "Abir Raza and Elarbi Badidi and Basma Badidi and \{Al Zahmi\}, Sarah",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 4th Joint International Conference on Deep Learning, Big Data and Blockchain, DBB 2023 ; Conference date: 14-08-2023 Through 16-08-2023",

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AU - Badidi, Elarbi

AU - Badidi, Basma

AU - Al Zahmi, Sarah

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Y1 - 2023

N2 - Recognition and extraction of license plate information from still images or videos are the basis of modern traffic and security systems. Automatic License Plate Recognition (ALPR) is transforming public safety and transportation in many ways. Such license plate recognition systems enable advanced solutions for toll roads, offer significant operational cost savings through automation, and even open up new market opportunities, such as license plate readers mounted on police vehicles. In this work, we implement license plate recognition based on convolutional neural networks to achieve high accuracy. You Only Look Once (YOLO) algorithm is employed for detection, while, an Optical Character Recognition (OCR) technique is used for text recognition. We use four variants of the YOLOv5 for license plate detection and the EasyOCR for license plate recognition. The results show that YOLOv5x (extra-large) achieves a Mean Average Precision (mAP) of nearly 82% in detecting vehicles and license plates in an image. Furthermore, the recognition of the letters on license plates is achieved with a confidence score of nearly 65%.

AB - Recognition and extraction of license plate information from still images or videos are the basis of modern traffic and security systems. Automatic License Plate Recognition (ALPR) is transforming public safety and transportation in many ways. Such license plate recognition systems enable advanced solutions for toll roads, offer significant operational cost savings through automation, and even open up new market opportunities, such as license plate readers mounted on police vehicles. In this work, we implement license plate recognition based on convolutional neural networks to achieve high accuracy. You Only Look Once (YOLO) algorithm is employed for detection, while, an Optical Character Recognition (OCR) technique is used for text recognition. We use four variants of the YOLOv5 for license plate detection and the EasyOCR for license plate recognition. The results show that YOLOv5x (extra-large) achieves a Mean Average Precision (mAP) of nearly 82% in detecting vehicles and license plates in an image. Furthermore, the recognition of the letters on license plates is achieved with a confidence score of nearly 65%.

KW - Convolutional neural networks

KW - Deep learning

KW - Object detection

KW - Object recognition

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YOLOv5 for Automatic License Plate Recognition in Smart Cities

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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