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W. Zhang, H. Sun, J. Zhou, X. Liu, Z. Zhang and G. Min, "DCNN Based Real-Time Adaptive Ship License Plate Recognition (DRASLPR)," 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Halifax, NS, Canada, 2018, pp. 1829-1834. doi: 10.1109/Cybermatics_2018.2018.00304

DCNN based real-time adaptive ship license plate recognition (DRASLPR)

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Author: Zhang, Weishan1; Sun, Haoyun1; Zhou, Jiehan2;
Organizations: 1Department of Software Engineering, China University of Petroleum Qingdao, China
2University of Oulu Finland
3School Computer and Communication Engineering, China University of Petroleum Qingdao, China
4Engineering Technology Research Institute, Huabei Oilfield Company, PetroChina Renqiu, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202003238869
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-03-23
Description:

Abstract

Ship license plate recognition is challenging due to the diversity of plate locations and text types. This paper proposes a DCNN-based (deep convolutional neural network) online adaptive real-time ship license plate recognition approach, namely, DRASLPR, which consists of three steps. First, it uses a Single Shot MultiBox Detector (SSD) to detect a ship. Then, it detects the ship license plate with a designed detector. Third, DRASLPR recognizes the ship license plate. The proposed DRASLPR has been deployed at Dongying Port, China and the running results show the effectiveness of DRASLPR.

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ISBN: 978-1-5386-7975-3
ISBN Print: 978-1-5386-7976-0
Pages: 1829 - 1834
DOI: 10.1109/Cybermatics_2018.2018.00304
OADOI: https://oadoi.org/10.1109/Cybermatics_2018.2018.00304
Host publication: Proceedings IEEE 2018 International Congress on Cybermatics - 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Conference: IEEE International Conference on Computer and Information Technology
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Deep learning
Image classification
Online adaptation
Ship license plate recognition
Text detection
Article
Funding: This work was supported in part by the Key Research Program of Shandong Province under Grant 2017GGX10140 and in part by the Fundamental Research Funds for the Central Universities(15CX08015A), National Natural Science Foundation of China (No. 61309024).
Copyright information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.