University of Oulu

W. Zhang, H. Sun, J. Zhou, X. Liu, Z. Zhang and G. Min, "Fully Convolutional Network Based Ship Plate Recognition," 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018, pp. 1803-1808,

Fully convolutional network based ship plate recognition

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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, 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, 0.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2020-04-28


Ship plate recognition is challenging due to variations of plate locations and text types. This paper proposes an effcient Fully Convolutional Network based Plate Recognition approach FCNPR, which uses a CNN (Convolutional Neural Network) to locate ships, then detects plate text lines with the fully convolutional network (FCN). The recognition accuracy is improved with integrating the AIS (Automatic Identification System) information. The actual FCNPR deployment demonstrates that it can work reliably with a high accuracy for satisfying practical usages.

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Series: IEEE International Conference on Systems, Man, and Cybernetics
ISSN: 2163-9590
ISSN-E: 2380-1360
ISSN-L: 2163-9590
ISBN: 978-1-5386-6650-0
ISBN Print: 978-1-5386-6651-7
Pages: 1803 - 1808
DOI: 10.1109/SMC.2018.00312
Host publication: IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
Conference: IEEE International Conference on Systems, Man, and Cybernetics
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
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).
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