Multinational License Plate Recognition Using Generalized Character Sequence Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Henry C. | - |
dc.contributor.author | Ahn S.Y. | - |
dc.contributor.author | Lee S.-W. | - |
dc.date.available | 2020-04-06T06:44:29Z | - |
dc.date.created | 2020-04-02 | - |
dc.date.issued | 2020-02 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/26360 | - |
dc.description.abstract | Automatic license plate recognition (ALPR) is generally considered a solved problem in the computer vision community. However, most of the current works on ALPR are designed to work on license plates (LP) from specific countries and use country-specific information which limits their practical applicability. Such ALPR systems require changes in the algorithm to work on other countries' LPs. Previous works on multinational LP recognition are tested on datasets from various countries that share the same LP layout. To address this issue, this study presents a deep ALPR system designed to be applicable to multinational LPs. The proposed approach consists of three main steps- LP detection, unified character recognition, and multinational LP layout detection. The system is mainly based on the you only look once (YOLO) networks. Particularly, tiny YOLOv3 was used for the first step whereas the second step uses YOLOv3-SPP- a version of YOLOv3 that consists of the spatial pyramid pooling (SPP) block. The localized LP is fed into YOLOv3-SPP for character recognition. The character recognition network returns the bounding boxes of the predicted characters and does not provide information about the sequence of the LP number. A LP number with an incorrect sequence is considered wrong. Thus, to extract the correct sequence, we propose a layout detection algorithm that can extract the correct sequence of LP numbers from multinational LPs. We collected our own Korean car plate (KarPlate) dataset and made it publicly available. The proposed system was evaluated on LP datasets from five countries which include South Korea, Taiwan, Greece, USA, and Croatia. In addition, a small dataset containing LPs from 17 countries was collected to evaluate the effectiveness of the multinational LP layout detection algorithm. The proposed ALPR system consumes about 42 ms per image on average for extracting LP number. Experimental results demonstrate the effectiveness of our ALPR system. © 2013 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.relation.isPartOf | IEEE Access | - |
dc.title | Multinational License Plate Recognition Using Generalized Character Sequence Detection | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000567611100018 | - |
dc.identifier.doi | 10.1109/ACCESS.2020.2974973 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.8, pp.35185 - 35199 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85080965778 | - |
dc.citation.endPage | 35199 | - |
dc.citation.startPage | 35185 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 8 | - |
dc.contributor.affiliatedAuthor | Henry C. | - |
dc.contributor.affiliatedAuthor | Ahn S.Y. | - |
dc.contributor.affiliatedAuthor | Lee S.-W. | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | License plate detection | - |
dc.subject.keywordAuthor | license plate recognition | - |
dc.subject.keywordAuthor | multinational license plate recognition | - |
dc.subject.keywordPlus | License plates (automobile) | - |
dc.subject.keywordPlus | Optical character recognition | - |
dc.subject.keywordPlus | Signal detection | - |
dc.subject.keywordPlus | Automatic license plate recognition | - |
dc.subject.keywordPlus | Detection algorithm | - |
dc.subject.keywordPlus | Generalized character | - |
dc.subject.keywordPlus | License plate detection | - |
dc.subject.keywordPlus | License plate recognition | - |
dc.subject.keywordPlus | Sequence detection | - |
dc.subject.keywordPlus | Specific information | - |
dc.subject.keywordPlus | Vision communities | - |
dc.subject.keywordPlus | Automatic vehicle identification | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.