基于卷积神经网络的污损遮挡号牌分类

doi:10.11896/JsJkx.191100089

计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 213-219.doi: 10.11896/JsJkx.191100089

• 计算机图形学 & 多媒体 • 上一篇下一篇

基于卷积神经网络的污损遮挡号牌分类

李林¹, 赵凯月¹, 赵晓永², 魏帅琴³, 张兵⁴

1 燕山大学信息与科学工程学院河北秦皇岛 066004;
2 北京信息科技大学信息管理学院北京 100097;
3 空军指挥学院教研保障中心北京 100097;
4 公安交通警察支队指挥中心河北沧州 061000

发布日期:2020-07-07
通讯作者: 赵凯月(zhaokaiyue9421@foxmail.com)
作者简介:lilin @ysu.edu.cn
基金资助:
北京市教育委员会科技计划一般项目(KM201711232018);国家自然科学基金项目(61502039);软件定义能源互联网的模型与优化控制研究项目;北京信息科技大学2019年度科技计划一般项目(KM201911232002)

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 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).

摘要/Abstract

摘要： 作为智能交通的重要组成部分之一,车牌识别在人们的日常生活中发挥着不可替代的作用,例如,生活中违规车辆常常因号牌污损或者故意遮挡等来躲避处罚,进一步加大了执法的难度,因此提高污损或遮挡车牌的识别效率依然是当今自动识别系统中的一项至关重要的任务。文中主要集中解决遮挡号牌的识别问题,其主要分为正常号牌、部分遮挡号牌、完全遮挡号牌以及未悬挂4种情况。传统的OCR算法在汉字、字符以及数字之间的识别上具有很高的准确性,当将其运用到识别车牌上后,虽然在正常号牌和部分遮挡号牌的检测上也都体现出了很好的识别效果,但对全部遮挡和未悬挂车牌的识别效果依然很差,随着人工智能的发展,使得其在全部遮挡和未悬挂号牌的识别上也能有更好的效果。因此,结合传统算法的优点,采用OCR技术并结合现今的深度学习算法,优化对污损车牌的识别效果。

关键词: OCR, 目标检测, 深度学习, 污损车牌, 智能交通

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

中图分类号:

TP311.5

李林, 赵凯月, 赵晓永, 魏帅琴, 张兵. 基于卷积神经网络的污损遮挡号牌分类[J]. 计算机科学, 2020, 47(6A): 213-219. https://doi.org/10.11896/JsJkx.191100089

LI Lin, ZHAO Kai-yue, ZHAO Xiao-yong, WEI Shuai-qin and ZHANG Bing. Contaminated and Shielded Number Plate Recognition Based on Convolutional Neural Network[J]. Computer Science, 2020, 47(6A): 213-219. https://doi.org/10.11896/JsJkx.191100089

参考文献

[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.

相关文章 15

[1]	饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277
[2]	汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108
[3]	徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204
[4]	王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099
[5]	郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077
[6]	姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046
[7]	刘冬梅, 徐洋, 吴泽彬, 刘倩, 宋斌, 韦志辉. 基于边框距离度量的增量目标检测方法 Incremental Object Detection Method Based on Border Distance Measurement 计算机科学, 2022, 49(8): 136-142. https://doi.org/10.11896/jsjkx.220100132
[8]	王灿, 刘永坚, 解庆, 马艳春. 基于软标签和样本权重优化的Anchor Free目标检测算法 Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization 计算机科学, 2022, 49(8): 157-164. https://doi.org/10.11896/jsjkx.210600240
[9]	孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061
[10]	胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092
[11]	程成, 降爱莲. 基于多路径特征提取的实时语义分割方法 Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction 计算机科学, 2022, 49(7): 120-126. https://doi.org/10.11896/jsjkx.210500157
[12]	侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018
[13]	周慧, 施皓晨, 屠要峰, 黄圣君. 基于主动采样的深度鲁棒神经网络学习 Robust Deep Neural Network Learning Based on Active Sampling 计算机科学, 2022, 49(7): 164-169. https://doi.org/10.11896/jsjkx.210600044
[14]	苏丹宁, 曹桂涛, 王燕楠, 王宏, 任赫. 小样本雷达辐射源识别的深度学习方法综述 Survey of Deep Learning for Radar Emitter Identification Based on Small Sample 计算机科学, 2022, 49(7): 226-235. https://doi.org/10.11896/jsjkx.210600138
[15]	刘伟业, 鲁慧民, 李玉鹏, 马宁. 指静脉识别技术研究综述 Survey on Finger Vein Recognition Research 计算机科学, 2022, 49(6A): 1-11. https://doi.org/10.11896/jsjkx.210400056

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于卷积神经网络的污损遮挡号牌分类

Contaminated and Shielded Number Plate Recognition Based on Convolutional Neural Network

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0