DCNN based Real-time Adaptive Ship License Plate Recognition (DRASLPR)

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.

Authors:
Zhang Weishan, Sun Haoyun, Zhou Jiehan, Liu Xin, Zhang Zhanmin, Min Guizhi

Publication type:
A4 Article in conference proceedings

Place of 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)

Keywords:
Deep learning, Image classification, Online adaptation, Ship license plate recognition, Text detection

Published:

Full citation:
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

DOI:
https://doi.org/10.1109/Cybermatics_2018.2018.00304

Read the publication here:
http://urn.fi/urn:nbn:fi-fe202003238869