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)
|Author:||Zhang, Weishan1; Sun, Haoyun1; Zhou, Jiehan2;|
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
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202003238869
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-03-23
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.
|Pages:||1829 - 1834|
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)
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
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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