SLPR: A Deep Learning Based Chinese Ship License Plate Recognition Framework

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 12; pp. 23831 - 23843
• Main Authors: Liu, Dekang, Cao, Jiuwen, Wang, Tianlei, Wu, Huahua, Wang, Jianzhong, Tian, Jiangmin, Xu, Fangyong
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Apexes; Deep learning; Entropy (Information theory); Feature extraction; License plate recognition; License plates; Licenses; Machine learning; Marine vehicles; perspective transformation; Recognition; ship identification; Ship license plate detection; Task analysis; Text recognition; Waterways
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3196814

Automatic ship license plate recognition (SLPR) for ship identification is of great significance to waterway shipping management. But few attention has been paid to SLPR in the past. In this paper, a novel cascaded Chinese SLPR framework consisting of the quadrangle-based ship license plate detection (QSLPD) algorithm and the rectification-based text recognition network (RTRNet) is developed. Concretely, in QSLPD algorithm, detection is performed based on the pyramid feature fusion architecture ameliorated by the proposed variable receptive field feature enhancement strategy and three task-specific output heads. In addition, a new loss function combining the dice coefficient and cross entropy is explored in the proposed SLPR which can generate significant improvement over the baseline. In RTRNet, regions of interest (RoIs) extraction and irregular text line rectification based on the vertices information predicted by QSLPD are performed before text recognition. Data augmentation are also applied to cope with the problem of limited text recognizer training data and the extremely imbalance distribution of corpus. Extensive experiments are carried out to demonstrate the reliability of the proposed cascaded SLPR framework, that can achieve the highest F-measure of 87.78% and 76.59% with IoU and TIoU metric on the collected dataset, surpasses many existing advanced methods.