Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6): 179-186.doi: 10.11896/jsjkx.240500064

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Ship License Plate Recognition Network Based on Pyramid Transformer in Transformer

WANG Teng1, XIAN Yunting1, XU Hao1, XIE Songqi1, ZOU Quanyi2   

  1. 1 School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,China
    2 School of Journalism and Communication,South China University of Technology,Guangzhou 510006,China
  • Received:2024-05-16 Revised:2024-09-05 Online:2025-06-15 Published:2025-06-11
  • About author:WANG Teng,born in 2000,postgradua-te.His main research interests include image processing and deep learning.
    XIAN Yunting,born in 1982,Ph.D,lab master.His main research interests include artificial intelligence and image processing.
  • Supported by:
    Guangdong Philosophy and Social Science Foundation Regular Project(GD24YXW02) and Youth Innovative Talent Projects of Guangdong Universities(2023KQNC005).

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Abstract: Ship identification is of great significance and widely used in the regulation of waterborne targets.As one of the important components of ship identification,accurate identification of ship name can make up for the shortcomings of traditional AIS identification methods and improve the accuracy of ship identification.Compared with the traditional Chinese text recognition,due to the complex water environment,large changes in light,serious corrosion of ship hulls,and non-standardized ship names,ship name images have low clarity,text mutilation,inconsistent font styles and other problems,which make ship name recognition difficult and low accuracy.In this paper,a lightweight recognition network based on Pyramid Transformer in Transformer is proposed to solve the problems in ship name recognition.Firstly,the input image is processed by a spatial transform network to correct the tilt of the ship name.Then,the Transformer in Transformer module is utilized to efficiently extract the multi-granularity features of the image.Finally,the text and radical are recognized at different scales.Experimental results show that the proposed algorithm has excellent performance in ship name recognition compared with other text recognition methods.The accuracy reaches 92.68% on CSLD dataset,94.50% on SCSLD dataset,and 66.34% on DCSLD dataset.At the same time,this method is characterized by a low number of parameters and a high frame rate.

Key words: Chinese text recognition, Ship license plate recognition, Deep learning, Scene text recognition, Transformer

CLC Number: 

  • TP183
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