车牌识别

A. .., J. .., Somya .., Surinder Kaur
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引用次数: 5

摘要

车牌识别(VLPR)是将计算机技术与智能交通系统(ITS)相结合的重要技术之一。然而,在大多数情况下,要成功识别车牌,首先要确定车牌的位置。车辆牌照识别系统被执法机构、交通管理机构、管制机构以及各种政府和非政府机构使用。VLPR用于各种商业应用,包括电子收费、个人安全、访客管理系统、停车管理和其他企业应用。因此,从车辆图像中计算车牌的正确定位是VLPR系统的一个重要步骤,这对整个系统的识别率和速度有很大的影响。在智能交通系统和图像识别领域,VLPR是一个热门话题。在这篇研究论文中,我们使用You Only Look Once (YOLO)-PyTorch深度学习架构来解决车牌检测问题。在本研究中,我们使用YOLO版本5来识别图像数据集中的单个类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle License Plate Recognition
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.
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CiteScore
2.00
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