第七章 总结
本文将车牌识别算法应用于物流仓储中，卷积神经网络这一深度学习技术的发展，让车牌识别的精度与速度又上升了一个台阶，因此本文将卷积神经网络与车牌识别相结合，设计了物流园中基于卷积神经网络的车牌识别管理系统。对物流园中出入车辆实现了自动化管理，在实际应用中有效提高物流园通行效率的同时，还提升了其安全性与可靠性。在此，本文所做的主要工作如下：
(1) 对本文系统所涉及的相关技术进行了研究，研究分为技术介绍与国内外现状。内容分为三个部分，即卷积神经网络技术，车牌识别技术以及车牌识别技术在物流中的应用。
(2) 在对相关技术进行分析研究之后，对基于卷积神经网络的车牌识别管理系统进行总体设计。首先对系统进行了需求分析，分别阐述了系统的功能需求与性能需求；在系统需求的基础上对系统的框架与功能进行了总体设计，对系统的各个功能模块进行了介绍；最后，为了系统可以方便的存储查询数据进行了数据库的设计。
(3) 对所使用的车牌识别技术进行优化，提出本文优化的基于卷积神经网络的车牌识别算法。阐述了本文提出的车牌定位与卷积神经网络结合的定位流程，并对字符识别中所应用的Yolo2 网络进行了参数优化。
(4) 对基于卷积神经网络的车牌识别管理系统进行详细设计。本文系统利用了 WebService 技术与 MVC 设计方法对系统的技术架构进行设计，并将整个系统分为服务中心客户端系统与门岗 web 端管理系统两个子系统。同时，对两个子系统中的主要功能模块进行了详细阐述，并对多个功能的逻辑流程进行了分析介绍。
(5) 对系统进行系统测试，测试分为功能测试与性能测试两个部分。首先对系统的软硬件环境进行介绍，并对系统的搭建过程进行了阐述，最后对系统的各个业务逻辑功能与车牌识别的性能进行了测试。
参考文献：
[1] C. L. P. Chen, "Big Data challenges, techniques, technologies, and applications and how deep learning can beused," 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design(CSCWD), Nanchang, 2016, pp. 3-3.
[2] J. Zeng, Y. Yu and B. Tian, "Landslide monitoring based on computer vision technology," 2014 InternationalConference on Information Science, Electronics and Electrical Engineering, Sapporo, 2014, pp. 629-635.
[3] Sheng Z. License Plate Recognition Technology Development Research and Improvement[J]. ManagementScience & Engineering, 2013, 7(2).
[4] Li H, Lin Z, Shen X, et al. A convolutional neural network cascade for face detection[C]// Computer Visionand Pattern Recognition. IEEE, 2015:5325-5334.
[5] 侯志强, 戴铂, 胡丹,等. 基于感知深度神经网络的视觉跟踪[J]. 电子与信息学报, 2016, 38(7):1616-1623.
[6] Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature,1986, 323(6088):533-536.
[7] Hinton G E, Osindero S, Teh YW.Afast learning algorithm for deep belief nets[M]. MIT Press, 2006.
[8] Yin B C, Wang W T, Wang L C. Review of Deep Learning[J]. Journal of Beijing University of Technology,2015, 41(1):48-59.
[9] Hua Y, Guo J, Zhao H. Deep Belief Networks and deep learning[C]// International Conference on IntelligentComputing and Internet of Things. IEEE, 2015:1-4.
[10] L?cun,Yann,Bottou,Leon,Bengio,Yoshua,etal.Gradient-basedlearningappliedtodocumentrecognition[J]Proceedings of the IEEE, 1998, 86(11):2278-2324.
[11] Kalchbrenner N, Grefenstette E, Blunsom P. A Convolutional Neural Network for Modelling Sentences[J].EprintArxiv, 2014, 1.
[12] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6):1229-1251.
[13] Zhao J, Ma S, Han W, et al. Research and Implementation of License Plate Recognition Technology[C]//Control and Decision Conference. IEEE, 2012:3768-3773.
[14] 李建春, 杨星, 刘伟. 车牌字符分割的研究现状及展望[J]. Computer Science & application, 2013,03(01):49-53.
[15] Nagare A P. License Plate Character Recognition System using Neural Network[J]. International Journal ofComputerApplications, 2011, 25(10):36--39.
[16] Qin L, Hu Y, Liu X. Precise Positioning of Image Recognition for Automobile License Plate Based onIntegrated Characteristics in Complex Environment[J]. International Journal of Digital Content Technology &ItsApplications, 2012, 6(23):809-816.
[17] Liu G. New Technology for License Plate Location and Recognition[C]// International Conference onIndustrial Control and Electronics Engineering. IEEE Computer Society, 2012:517-519.
[18] Yu S, Li B, Zhang Q, et al. A novel license plate location method based on wavelet transform and EMDanalysis[J]. Pattern Recognition, 2015, 48(1):114-125.
[19] Wang Y R, Lin W H, Horng S J. A sliding window technique for efficient license plate localization based ondiscrete wavelet transform[J]. Expert Systems withApplications, 2011, 38(4):3142-3146.
[20] Wang,Yutao, Qin, et al. Recognition of License Plate Character Based on Wavelet Transform and GeneralizedRegression Neural Network[C]// 中国控制与决策会议. 2012:1881-1885.
[21] Wang C M, Su C Y. Fast license plate location and recognition using wavelet transform in android[C]//Industrial Electronics andApplications. IEEE, 2012:1035-1038.
[22] Yu S F, Xu Z J, Zhang B, et al.Anovel algorithm for license plate location based on the RGB features and thetexture features[C]// International Conference on Biomedical Engineering and Informatics. IEEE, 2013:156-159.
[23] Tan C, Cao J. An Algorithm for License Plate Location Based on Color and Texture[C]// InternationalConference on Intelligent Human-Machine Systems and Cybernetics. IEEE Computer Society, 2013:356-359.
[24] Wu Y, Liu S, Wang X. License plate location method based on texture and color[C]// IEEE InternationalConference on Software Engineering and Service Science. IEEE, 2013:361-364.
[25] Yu X, Cao H, Lu H. Algorithm of license plate localization based on texture analysis[C]// InternationalConference on Transportation, Mechanical, and Electrical Engineering. IEEE, 2012:259-262.
[26] Cao X Y, Song Y J, Xu T. License Plate Location Based on Projection Method and Genetic Algorithm[M]//Proceedings of the 2012 International Conference on Information Technology and Software Engineering.Springer Berlin Heidelberg, 2013:531-539.
[27] Wang X, Hao L, Wu L, et al. Genetic algorithm based neural network for license plate recognition[C]//International Conference onAdvances in Neural Networks. Springer-Verlag, 2013:391-400.
[28] 廉宁 , 徐艳蕾 , LianNing, 等 . 基于数学形态学和颜色特征的车牌定位方法 [J]. 图学学报 , 2014,35(5):774-779.
[29] Lian N, Xu,Yan Lei. Method of License Plate Location Based on Mathematical Morphology and ColorCharacteristics[J].Applied Mechanics & Materials, 2014, 602-605:2263-2266.
[30] 郭克友, 贾海晶 , 郭晓丽. 卷积神经网络在车牌分类器中的应用 [J]. 计算机工程与应用 , 2017,53(14):209-213.
[31] Dong J, Sun M, Liang G, et al. The Improved Neural Network Algorithm of License Plate Recognition[J].International Journal of Signal Processing Image Processing & Pattern Recognition, 2015, 8(5):196-205.
[32] XieG, RenX. License PlateLocation Basedon SimplifiedPulse CoupledNeuralNetwork andComprehensive Feature[C]// International Conference on Computer Science and Intelligent Communication. 2015.
[33] WangB,FangY,SunC.Imagesegmentationalgorithmbasedonhigh-dimensionfuzzycharacterandrestrainedclustering network[J]. 系统工程与电子技术(英文版), 2014, 25(2):298-306.
[34] Wang L, Zhang G. Cluster Ensemble Based Image Segmentation Algorithm[J]. International Journal ofAdvanced Robotic Systems, 2015, 10(4):1.
[35] Li X, Xu S C, You Y C, et al. Segmentation method for personalized American car plate based on clusteringanalysis[J]. Journal of Zhejiang University, 2012, 46(12):2155-2159.
[36] Wang H, Liu G, Ke H, et al. A Vehicle License Plate Detection Method Based on Clustering Analysis[C]//InternationalConferenceonComputer ScienceandService System. IEEEComputer Society, 2012:1413-1416.
[37] Xia H, Liao D. The study of license plate character segmentation algorithm based on vetical projection[C]//International Conference on Consumer Electronics, Communications and Networks. IEEE, 2011:4583-4586.
[38] Cheng G T. Character Segmentation Based on Vertical Projection and Template Matching[J]. Journal of NorthChina Institute ofAerospace Engineering, 2013.
[39] Miao L. License plate character segmentation algorithmbased on variable-length template matching[C]// IEEE,International Conference on Signal Processing. IEEE, 2013:947-951.
[40] Zhou L, Wang X H, Tao-Tao Y E, et al. License plate character segmentation algorithm based on improvedtemplate matching[J]. Information Technology, 2014.
[41] Yang X, Zhao Y, Fang J, et al. A license plate segmentation algorithm based on MSER and templatematching[C]// International Conference on Signal Processing. IEEE, 2015:1195-1199.
[42] Wang Z H, Guo C, Liu H M. News Image Caption Line Segmentation Algorithm Based on TemplateMatching[J]. Journal of Beijing University of Posts & Telecommunications, 2016.
[43] Pei M T, Wang Y J, Jia Y D, et al. License plate character segmentation based on multiple scale templatesmatching and part-based model[J]. Beijing Ligong Daxue Xuebao/transaction of Beijing Institute ofTechnology, 2014, 34(9):961-965 and 971.
[44] 周律, 王新华, 叶涛涛,等. 基于改进模版匹配的车牌字符分割[J]. 信息技术, 2014(9):81-85.
[45] Li X, Lv X, Wang S, et al. Notice of RetractionResearch on the recognition algorithm of the license platecharacter based on the multi-resolution template matching[C]// International Conference on New Trends inInformation Science and Service Science. IEEE, 2010:360-363.
[46] Zhang Z J, Yuan L, Yuan W Q. Meter character recognition method based on gray template matching[C]//Control Conference. IEEE, 2010:2987-2990.
[47] Antony P J, Savitha C K, Ujwal U J. Haar features based handwritten character recognition system for Tuluscript[C]// IEEE International Conference on Recent Trends in Electronics, Information & CommunicationTechnology. IEEE, 2017:65-68.
[48] Madushanka P T C, Bandara R, Ranathunga L. Sinhala handwritten character recognition by using enhancedthinning and curvature histogram based method[C]// IEEE, International Conference on Signal and ImageProcessing. IEEE, 2017:46-50.
[49] Jyothi J, Manjusha K, Kumar M A, et al. Innovative feature sets for machine learning based Telugu characterrecognition[J]. 2015.
[50] Arafat A, Khaled M, Abdulrab I, et al. Optical Character Recognition based on Genetic Algorithms an Machine Learning[J]. International Journal of ComputerApplications, 2017, 172(2):33-36.
[51] Jiang F, Liu H, Bai B, et al. Automatic IC Character Recognition System for IC Test Handler Based o SVM[C]// International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, 2016.
[52] Dogra S, Prakash C. PEHCHAAN: HINDI HANDWRITTEN CHARACTER RECOGNITION SYSTE BASED ON SVM[J]. International Journal on Computer Science & Engineering, 2012, 4(5):718-722.
[53] Vapnik V. SVM method of estimating density, conditional probability, and conditional density[C]// IEEEInternational Symposium on Circuits and Systems, 2000. Proceedings. ISCAS. IEEE, 2000:749-752 vol.2.
[54] 张旭兰. 基于BP人工神经网络的车牌字符识别优化算法[J]. 计算机工程与应用, 2015, 48(1):182-185.
[55] Dong J, Zheng B, Yang Z. Character recognition of license plate based on convolution neural network[J].Journal of ComputerApplications, 2017.
[56] Zhen-Wen H U. Character Recognition of Vehicle’s License Plate Based On BP Neural Network[C]/ International Conference onArtificial Intelligence Science and Technology. 2017:590-595.
[57] Fang D, FengY, LiY, etal. Design of Model of Logistics Distribution Center Information ManagementSyste Based on RFID[J]. Logistics Technology, 2014.
[58] Weihua G, Tingting Z,Yuwei Z. On RFIDApplication in the Information System of Rail Logistics Center[C]//International Workshop on Education Technology & Computer Science. 2013:308-311.
[59] Stindt D. An Environmental Management Information System for Improving Reverse Logistics Decision-Making[J]. 2014, 8760:163-177.
[60] Jiang J. Design of Information Management System for Logistics Park Bulk Transportation Vehicles[J].Logistics Technology, 2014.
[61] Qiao X F. Xuangang steel logistics information management system[J]. Metallurgical Industry Automation,2013.
[62] Redmon J, FarhadiA. YOLO9000: Better, Faster, Stronger[J]. 2016:6517-6525.
[63] Anderson S M, Mendoza B S, Carriles R.Ab initio Calculation of the Depth‐Dependent Optical Reflectance From Layer‐by‐LayerAtomic Disorder[J]. Physica Status Solidi, 2017:1700487.
[64] Jin P, Yao J. Design and realization of college service center system based on MVC[C]// Advanced Research and Technology in IndustryApplications. IEEE, 2014.
[65] Li T, Geng H. Research on the Performance Management Problems in Local Governments' Administrative Service Centers and the Countermeasures[C]// International Conference on Economics, Finance and Statistics. 2017.
[66] T?lke J. Implementation of a Lattice Boltzmann kernel using the Compute Unified Device Architecturedeveloped by nVIDIA[J]. Computing & Visualization in Science, 2010, 13(1):29-39.
[67] Mielikainen J, Price E, Huang B, et al. GPU Compute Unified Device Architecture (CUDA)-base Parallelization of the RRTMG Shortwave Rapid Radiative Transfer Model[J]. IEEE Journal of Selected Topic inApplied Earth Observations & Remote Sensing, 2016, 9(2):921-931.
[68] Bradski G,DaeblerA. Learning OpenCV. Computer vision with OpenCVlibrary[J]. UniversityofArizona Usa Since, 2012.
[69] Chipantasi D J M, Erazo N D R V. Augmented Reality for Automatic Identification and Solving SudokuPuzzles Based on Computer Vision[C]// International Conference on Image and Graphics. Springer, Cham, 2015:603-613.
[70] Widenius M. Mysql Reference Manual[M]. O'Reilly &Associates, Inc. 2002.
[71] Renzis A D, Garriga M, Flores A, et al. Assessing readability of Web service interfaces[C]// Computin Conference. IEEE, 2017:1-12.
