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Fusion: Practice and Applications
Volume 4 , Issue 1, PP: 15-21 , 2021 | Cite this article as | XML | Html |PDF

Title

Vehicle License Plate Recognition

Authors Names :   Aman Jain   1 *     Jatin Gupta   2     Somya Khandelwal   3     Surinder Kaur   4  

1  Affiliation :  Information Technology Bharati Vidyapeeth’s College Of Engineering, New Delhi, India

    Email :  aman.jain744@gmail.com


2  Affiliation :  Information Technology Bharati Vidyapeeth’s College Of Engineering, New Delhi, India

    Email :   jatingupta5jan@gmail.com


3  Affiliation :  Information Technology Bharati Vidyapeeth’s College Of Engineering, New Delhi, India

    Email :  omyakhandelwal27@gmail.com


4  Affiliation :  Information Technology Bharati Vidyapeeth’s College Of Engineering, New Delhi, India

    Email :  kaur.surinder@bharaividyapeeth.edu



Doi   :   https://doi.org/10.54216/FPA.040102

Received February 02, 2021 Accepted June 05, 2021

Abstract :

One of the most significant parts of integrating computer technologies into intelligent transportation systems (ITS) is vehicle license plate recognition (VLPR). In most cases, however, to recognize a license plate successfully, the location of the license plate is to be determined first. Vehicle License Plate Recognition systems are used by law enforcement agencies, traffic management agencies, control agencies, and various government and non-government agencies. VLPR is used in various commercial applications, including electronic toll collecting, personal security, visitor management systems, parking management, and other corporate applications. As a result, calculating the correct positioning of a license plate from a vehicle image is an essential stage of a VLPR system, which substantially impacts the recognition rate and speed of the entire system. In the fields of intelligent transportation systems and image recognition, VLPR is a popular topic. In this research paper, we address the problem of license plate detection using a You Only Look Once (YOLO)-PyTorch deep learning architecture. In this research, we use YOLO version 5 to recognize a single class in an image dataset.

Keywords :

IOU; ITS; LPR; VLPR; YOLO v5; YOLO v5s

References :

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Cite this Article as :
Aman Jain , Jatin Gupta , Somya Khandelwal , Surinder Kaur, Vehicle License Plate Recognition, Fusion: Practice and Applications, Vol. 4 , No. 1 , (2021) : 15-21 (Doi   :  https://doi.org/10.54216/FPA.040102)