An Effective Method for Yemeni License Plate Recognition Based on Deep Neural Networks

被引：2

作者：

Taleb, Hamdan ^{[1
,2
]}

Li, Zhipeng ^{[1
]}

Yuan, Changan ^{[3
,4
]}

Wu, Hongjie ^{[5
]}

Zhao, Xingming ^{[6
]}

Ghanem, Fahd A. ^{[7
,8
]}

机构：

[1] Tongji Univ, Sch Elect & Informat Engn, Inst Machine Learning & Syst Biol, Shanghai 201804, Peoples R China

[2] Aljanad Univ Sci & Technol, Dept Informat Technol, Coll Engn & Informat Technol, Taizi, Yemen

[3] Guangxi Acad Sci, Nanning 530007, Peoples R China

[4] Guangxi Acad Sci, Guangxi Key Lab Human Machine Interact & Intellig, Nanning, Guangxi, Peoples R China

[5] Suzhou Univ Sci & Technol, Sch Elect & Informat Engn, Suzhou 215009, Peoples R China

[6] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence IS, Shanghai 200433, Peoples R China

[7] Univ Mysore, PES Coll Engn, Dept Comp Sci & Engn, Mandya, India

[8] Hodeidah Univ, Dept Comp Sci, Coll Educ Zabid, Hodeidah, Yemen

来源：

INTELLIGENT COMPUTING METHODOLOGIES, PT III | 2022年 / 13395卷

基金：

中国国家自然科学基金; 国家重点研发计划;

关键词：

License plate detection; Character recognition; Deep neural networks; PERSON REIDENTIFICATION;

D O I：

10.1007/978-3-031-13832-4_26

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As the existing publicly license plate recognition (LPR) datasets available for training are restricted and almost non-existent in some countries, for example, some developing countries. In this paper, first we present the first Yemeni License Plate dataset (Y-LPR dataset) includes vehicles and license plate images for Yemeni license plate detection and recognition. Second, we propose a new LPR method for license plate detection and Recognition. It consists of two key stages: First, License plate detection from images based on the latest state-of-the-art deep learning-based detector which is YOLOv5. Second, Yemeni Character and number recognition based on the CRNN model. Experimental results show that our method is effective in detecting and recognizing license plates.

引用

页码：304 / 314

页数：11

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