Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 213-219.doi: 10.11896/JsJkx.191100089

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Contaminated and Shielded Number Plate Recognition Based on Convolutional Neural Network

LI Lin1, ZHAO Kai-yue1, ZHAO Xiao-yong2, WEI Shuai-qin3 and ZHANG Bing4   

  1. 1 College of Information and Science Engineering,Yanshan University,Qinhuangdao,Hebei 066004,China
    2 School of Information Management,BeiJing University of Information Technology,BeiJing 100097,China
    3 Teaching and Research Support Center of Air Force Command College,BeiJing 100097,China
    4 Command Center of Police Traffic Detachment Cangzhou,Cangzhou,Hebei 061000,China
  • Published:2020-07-07
  • About author:LI Lin, born in 1977, Ph.D, associate professor.His main research intrests include embedded system and signal processing.
    ZHAO Kai-yue, born in 1993, postgra-duate.His main research intrests include machine learning and pattern re-cognition.
  • Supported by:
    This work was supproted by the General ProJect of Science and Technology Plan of BeiJing Municipal Education Commission (KM201711232018),National Natural Science Foundation ProJect (61502039),Research ProJect of Model and Optimization Control of Software Defined Energy Internet and General ProJect of Science and Technology Plan of BeiJing University of Information Technology in 2019 (KM201911232002).

Abstract: As one of the important components of intelligent transportation,license plate recognition plays an irreplaceable role in people’s daily life.For example,in daily life,illegal vehicles often avoid punishment because of the number plate contamination and occlusion,which further increases the difficulty of law enforcement.Therefore,improving the recognition efficiency of contaminated license plate is still a crucial issue in today’s automatic recognition system.The paper mainly focuses on the recognition of shielded number plate.There are four main cases:normal number plate,partially shielded number plate,completely shielded number plate and not hanging plate.The traditional OCR algorithm has a high accuracy in the recognition of Chinese characters,characters and numbers.When it is applied to the recognition of license plates,although the detection of normal and partial shielded license plates shows a good recognition effect,the recognition effect of completely shielded number plates and not hanging license plates is still very poor.With the development of artificial intelligence,it is possible to get better recognition on completely shielded plates and not hanging plates.Therefore,combined with the advantages of traditional algorithms,this paper adopted OCR technology and the current deep learning algorithm to optimize the recognition effect of stained license plate.

Key words: Intelligent transportation, Target detection, Contaminated license plate, Deep learning, OCR

CLC Number: 

  • TP311.5
[1] LI C Q.Research on Key Technologies of Fuzzy License Plate Image Recognition in Monitoring Video .NanJing:NanJing University of Posts and Telecommunications,2017.
[2] HU Z P,CHEN J L,WANG M,et al.New Progress of Convolutional Neural Network Classification Model in Pattern Recognition .Journal of Yanshan University,2015,39(4):283-291.
[3] LE C Y,BOTTOU L,BENGIO Y,et al.Gradient-Based learning applied to document recognition//Proc.of the IEEE.1998:2278-2324.
[4] BU Y J.Research on Algorithms of License Plate RecognitionSystem Based on Computer Vision .University of Electronic Science and Technology,2015.
[5] WANG Z.Research and Improvement of License Plate Recognition System .Shenyang:Shenyang University of Technology,2016.
[6] DING M L,DING M Y,NIU Y L,et al.A Method of Identifying Commodity Parameters in Pictures Based on Deep Learning .Journal of Software,2018,29(4):1039-1048.
[7] ZHANG S L.Research on Classification of Vehicle Brands Based on Vision .Dalian:Dalian University of Technology,2015.
[8] ZHANG S.Research on Docker Fault-tolerant Performance Monitoring and Adaptive Pre-replication Checkpoint Technology .Nanning:Guangxi University,2017.
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