文章基本信息

标题：Classification and Comparison of License Plates Localization Algorithms
本地全文：下载
作者：Mustapha Saidallah ; Fatimazahra Taki ; Abdelbaki El Belrhiti El Alaoui 等
期刊名称：Signal & Image Processing : An International Journal (SIPIJ)
印刷版ISSN：2229-3922
电子版ISSN：0976-710X
出版年度：2021
卷号：12
期号：2
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The Intelligent Transportation Systems (ITS) are the subject of a world economic competition. They are the application of new information and communication technologies in the transport sector, to make the infrastructures more efficient, more reliable and more ecological. License Plates Recognition (LPR) is the key module of these systems, in which the License Plate Localization (LPL) is the most important stage, because it determines the speed and robustness of this module. Thus, during this step the algorithm must process the image and overcome several constraints as climatic and lighting conditions, sensors and angles variety, LPs’ no-standardization, and the real time processing. This paper presents a classification and comparison of License Plates Localization (LPL) algorithms and describes the advantages, disadvantages and improvements made by each of them.
关键词：License Plate Localization;LPL constraints;performance;real time;multi-localization;convolutional neural network;deep learning