[1]吴聪,殷浩,黄中勇,等. 基于人工神经网络的车牌识别[J].计算机技术与发展,2016,26(12):160-163.
 WU Cong,YIN Hao,HUANG Zhong-yong,et al. Vehicle Plate Recognition Based on Artificial Neural Network[J].,2016,26(12):160-163.
点击复制

 基于人工神经网络的车牌识别()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年12期
页码:
160-163
栏目:
应用开发研究
出版日期:
2016-12-10

文章信息/Info

Title:
 Vehicle Plate Recognition Based on Artificial Neural Network
文章编号:
1673-629X(2016)12-0160-04
作者:
 吴聪殷浩黄中勇刘罡
湖北工业大学 光庭实验室
Author(s):
 WU CongYIN HaoHUANG Zhong-yongLIU Gang
关键词:
  车牌识别神经网络机器学习 遗传算法
Keywords:
 license plate recognitionneural networkmachine learninggenetic algorithm
分类号:
TP301.6
文献标志码:
A
摘要:
 车牌作为不同车辆的唯一标识,其识别技术是计算机视频图像在车辆牌照识别方面的一种重要应用,在各种场合是识别汽车身份的重要途径。由于现阶段技术的不断提升,识别过程中的问题也不断涌现,而在车牌预处理、分割以及识别阶段中,车牌识别是现代交通系统中非常重要的功能模块,而其关键因素在于汉字、数字以及字母的识别。通过提高车牌的识别率来提高交通部门的工作效率。目前,人工神经网络因其优越性被广泛应用于各种图像识别中,但因其收敛速度慢,运耗时间长,对实际应用产生了很大的限制。采用遗传算法与神经网络相结合的方法并进行了仿真,实验结果表明,该方法对车牌有很好的识别作用,具有时效性和鲁棒性。
Abstract:
 License plate as a different unique identification of the vehicle,its identification technology is an important application for com-puter video image in the license plates recognition,which is an important approach to identify a car for a wide range of situations. Due to the improving of the technology at present,the problems are also emerging in the process of recognition. In the stage of preprocessing, segmentation and recognition of the license plate,license plate recognition is an important function module in modern traffic system,and its key factor lies in the identification of Chinese characters,numbers and letters. By improving the license plate recognition rate,the work-ing efficiency of the transport sector is improved. At present,the artificial neural network is widely used because of its superiority in all sorts of image recognition,but because of its slow convergence speed and long time consuming,a lot of restrictions are produced in actual applications. By adopting the combination of genetic algorithm and neural network,the simulation results show that the experimental re-sults of the license plate has good recognition effect,which proves that the method is effective and robust.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
 ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(12):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
 LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(12):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
 HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(12):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
 HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(12):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
 LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(12):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
 WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(12):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
 LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(12):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
 SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(12):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
 YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(12):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
 YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(12):47.

更新日期/Last Update: 2017-02-04