DCNN based real-time adaptive ship license plate recognition (DRASLPR)
Zhang, Weishan; Sun, Haoyun; Zhou, Jiehan; Liu, Xin; Zhang, Zhanmin; Min, Guizhi (2019-06-03)
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
© 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.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe202003238869
Tiivistelmä
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.
Kokoelmat
- Avoin saatavuus [38865]