Neural Network-Powered License Plate Recognition System Design

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

Vehicle detection based on information fusion of vehicle symmetrical contour and license plate position

An efficient vehicle detection approach is proposed for traffic surveillance images, which is based on information fusion of vehicle symmetrical contour and license plate position. The vertical symmetry axis of the vehicle contour in an image is. first detected, and then the vertical and the horizontal symmetry axes of the license plate are detected using the symmetry axis of the vehicle contour as a reference. The vehicle location in an image is determined using license plate symmetry axes and the vertical and the horizontal projection maps of the vehicle edge image. A dataset consisting of 450 images （15 classes of vehicles） is used to test the proposed method. The experimental results indicate that compared with the vehicle contour-based, the license plate location-based, the vehicle texture-based and the Gabor feature-based methods, the proposed method is the best with a detection accuracy of 90.7% and an elapsed time of 125 ms.

Vehicle License Plate Character Segmentation

Vehicle license plate (VLP) character segmentation is an important part of the vehicle license plate recognition system (VLPRS).This paper proposes a least square method (LSM) to treat horizontal tilt and vertical tilt in VLP images.Auxiliary lines are added into the image (or the tilt-corrected image) to make the separated parts of each Chinese character to be an interconnected region.The noise regions will be eliminated after two fusing images are merged according to the minimum principle of gray values. Then,the characters are segmented by projection method (PM) and the final character images are obtained.The experimental results show that this method features fast processing and good performance in segmentation.

Automatic Vehicle License Recognition Based on Video Vehicular Detection System

Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented. Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold. The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%. When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system＇s requirement even under the noisy circumstance or uneven illumination.

Study on the vehicle license plate tilt correction

The shortage of current different approaches of the vehicle license plate(VLP) tilt correction is analyzed in the paper and a new rotary correction method put forward based on the former ways of the VLP tilt correction in the horizontal direction and the vertical direction Owing to the VLP tilt taking place in the vertical direction,the array of the image’s pixels of the same column is broken,and even different rows come into being superposition.The VLP tilt taking place in the horizontal direction,by which the array of the image’s pixels of the same row broken,and so much as different columns come into being superposition.

Deep Learning Based License Plate Number Recognition for Smart Cities

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

Multi-Object Detection of Chinese License Plate in Complex Scenes

Multi-license plate detection in complex scenes is still a challenging task because of multiple vehicle license plates with different sizes and classes in the images having complex background.The edge features of high-density distribution and the high curvature features of stroke turning of Chinese character are important signs to distinguish Chinese license plate from other objects.To accurately detect multiple vehicle license plates with different sizes and classes in complex scenes,a multi-object detection of Chinese license plate method based on improved YOLOv3 network was proposed in this research.The improvements include replacing the residual block of the YOLOv3 backbone network with the Inception-ResNet-A block,imbedding the SPP block into the detection network,cutting the redundant Inception-ResNet-A block to suit for the multi-license plate detection task,and clustering the ground truth boxes of license plates to obtain a new set of anchor boxes.A Chinese vehicle license plate image dataset was built for training and testing the improved network,and the location and class of the license plates in each image were accurately labeled.The dataset has 62,153 pieces of images and 4 classes of China vehicle license plates,almost images have multiple license plates with different sizes.Experiments demonstrated that the multilicense plate detection method obtained 83.4%mAP,98.88%precision,98.17%recall,98.52 F1 score,89.196 BFLOPS and 22 FPS on the test dataset,and whole performance was better than the other five compared networks including YOLOv3,SSD,Faster-RCNN,EfficientDet and RetinaNet.

在途危化品运输车辆智能识别系统设计与数据提取

危化品车辆抓拍识别是加强危险品运输监管,保障社会公共安全的关键。基于图像处理和极限学习机,提出了一种在途危化品运输车辆抓拍识别系统及算法。对抓拍危化品车辆图像二值化处理,通过模糊算法和形态学膨胀操作提取车牌区域。对提取的区域滤波处理和形态学膨胀操作,提取车牌字符特征。将提取的车牌字符特征作为极限学习机的输入,危化品车辆车牌的33个字母数字作为输出,达到危化品车辆车牌识别的目的。结果表明,所提出的算法对危化品车辆所处环境具有更好的适应性,同时具有更高的抓拍识别精度。这对危化品车辆行驶管理具有一定的实用价值。

限行政策对外地牌照车主出行选择行为的影响研究——以深圳市为例

根据国内典型限行政策的内容,分析了外地牌照车主在限行政策下的应对行为,在此基础上研究了他们在限行时段的出行决策过程。以深圳市外地牌照车主为调查对象,进行出行选择行为问卷调查。基于随机效用理论,建立外地牌照车主出行选择行为的两阶段Logit模型,同时对外地牌照车主的“改变出行方式”与“改变出行时间”的选择行为进行分析,结合个体社会经济属性、出行属性分析两阶段出行行为选择的影响因素。模型可为限行政策效果的评估及优化提供依据和参考。

融合出行拓扑与序列分析的车辆时空出行模式挖掘

城市小汽车出行的时空特性是支撑城市交通规划设计与交通需求管理的重要基础。针对传统的以集计数据或抽样数据研究的局限性,本文基于车牌识别数据,全量感知车辆出行活动,分析城市中个体车辆的出行时空模式。首先,从数据中提取并分离车辆出行链,获得小汽车出行的时间、空间、频率和拓扑特征,根据各时段停留点构造车辆出行活动序列。其次,融合兴趣点(Point of Interest, POI)数据识别出行起讫点关联的土地利用特性作为停留点特征,在出行活动序列上应用k-modes聚类算法挖掘出常规通勤模式、特殊通勤模式、短时活动模式和外来办事模式这4类30种小汽车出行模式。最后,对每一类模式的群体规模、特征和典型出行行为进行详细地分析讨论。结果表明,95%的车辆出行活动可以用不多于3条边组成的简单拓扑结构表示,其中,约30%的车辆可构造出行活动序列,并用k-modes聚类算法有效分离出各类机动车全天出行的时空模式。工作日车辆出行主要表现为常规通勤模式,休息日则以短时活动模式为主。通过对个体车辆的微观行为分析,结合出行拓扑结构和出行活动序列进行出行模式的挖掘,能够全面地反映城市机动车出行的实际情况,为精细化机动车出行行为分析与管控策略制定提供理论支撑。

基于递阶优化的城市区域路网交通控制

城市道路拥堵问题随着城市机动化的快速发展日益凸显,采用合理的控制手段提高区域路网效率成为急需解决的问题。以长沙市部分路网作为研究对象,选取连续5个工作日的早高峰流量数据进行研究。在VISSIM仿真软件中根据抽象路网搭建仿真路网,将仿真路网中的交通数据与实际道路车牌识别数据相匹配,以交叉口通过车辆数为评价标准计算仿真路网与实际道路的误差率,保证仿真路网真实性。采用反压力值作为道路反馈指标,计算路网中的压力分布情况。基于周期稳定性,将研究范围内的交叉口划分为2类,以大小周期的方式进行信号配时优化,考虑交通高峰期道路饱和度偏高的情况,将传统韦伯斯特算法进行改进后作为控制方法。采用递阶优化的思想进行控制策略计算。对控制策略进行仿真验证,在结果中从路网和车辆2个方面选取在网车辆数、到达车辆数、车均延误和车辆速度四4个指标对优化前后的路网状况进行评价。优化后的路网较原始路网在到达车辆数和车辆速度方面有所提升,在网车辆数和车均延误有所下降。同时,针对不同饱和度的交叉口,控制策略在4个评价指标方面也有相同的优化趋势。研究结果表明,基于递阶优化的路网交通控制能够提高路网的通行效率,改善路网运行环境。

新能源汽车制造商竞争与竞合策略选择研究

本文针对具有竞争关系的两新能源汽车制造商在动力电池供应方面是否合作的情形,探究新能源汽车制造商竞争与竞合策略的选择问题。考虑新能源汽车制造商的三种策略:竞争策略、批发竞合策略以及专利许可竞合策略,本文运用非合作博弈理论对三种策略分别构建博弈模型,比较三种策略下的均衡结果,发现:(1)处于上游的新能源汽车制造商的谈判能力较低或谈判能力较强且替代程度较高时,批发竞合策略下动力电池的批发价格与替代程度呈正相关性;否则呈负相关性;(2)专利许可竞合策略下动力电池技术专利的入门费与替代程度呈负相关性,而提成费与替代程度则呈正相关性;(3)新能源汽车制造商的最优策略选择取决于新能源汽车制造商的谈判能力、动力电池的成本差异以及新能源汽车的替代程度;(4)当竞合策略为最优策略时,新能源汽车制造商在一定条件下能够实现利润的帕累托改进,同时降低新能源汽车的整车价格。

基于集成学习的信号控制交叉口排队长度估计

基于电子警察(LPR)数据和网联车辆轨迹数据,提出了一种基于集成学习的信号控制交叉口排队长度估计方法。通过分析不同数据条件下估计方法的适用条件和精度水平,运用随机森林方法设计集成学习器,并构建电子警察和网联车辆轨迹感知信息及不同方法估计结果和真实排队长度之间的非线性映射关系。仿真结果表明:本方法的平均绝对误差为1.3 m·周期^(-1)·车道^(-1),平均绝对百分比误差为1.4%。

基于色彩空间的新能源汽车识别方法

为适应汽车使用结构的变化,越来越多的停车场设有新能源汽车停车位,并为其提供充电服务。但由于车位紧张,该停车位常被燃油车占用,导致新能源汽车无法充电。针对这一问题,提出了一种基于色彩空间的新能源汽车识别方法。该方法不仅能正确定位和识别车牌号,还能对车辆类型进行分类,是解决新能源汽车停车难问题的有效方法。首先,采用车牌几何特征与颜色特征相结合的方法对车牌进行定位。然后,通过色调饱和度明度(HSV)色彩空间识别出车辆类型。最后,采用基于残差网络(ResNet)模型的字符识别方法得到车牌号。实验表明,该方法车辆类型识别正确率为96.6%,车牌号识别正确率为97.53%。

基于车牌识别大数据的外地车辆出行特征分析

基于深圳市860个道路视频卡口的车牌识别大数据,总结分析深圳市“三轮”外地车辆管控政策的发展历程及相关影响,并提出车牌识别基础数据的预处理方法以及基于GIS的外地车辆交通出行特征分析方法。围绕“限外”和“取消限外”2种政策影响情形,从时间、空间、车辆属性、车辆使用等多个维度对车牌识别数据进行深入挖掘,定量分析外地车辆使用的总体特征及时空分布特征,深入研判车辆管控政策对外地车出行特征的影响。相关结论为深圳外地车辆管控政策的制定和调整提供了数据支撑,也为其他城市的外地车辆出行特征分析提供参考。

基于Relief算法的智能车辆牌照模糊识别方法

由于当前已有方法未能对车牌照进行降维处理,导致车牌照识别结果不准确,为此,提出一种基于Relief算法的智能车辆牌照模糊识别方法。采用Relief算法计算不同车牌图像特征的权重系数,对特征集进行降维处理。通过序列视频图像对智能车牌进行增强处理,利用全卷积网络对车牌照显著区域进行检测,粗略提取图像中的显著区域,使用滑动窗方法对候选区域车牌进行精准检测,定位车牌准确位置,加入字符的上下文信息,对字符进行精确检测和识别,最终实现智能车辆牌照模糊识别。仿真实验结果表明,所提方法可获取高精度的车牌照识别结果。

企业内部无人停车场管理系统设计与实现

为了提高企业内部停车场的管理效率和用户停车体验,本文设计了一款基于微信小程序+Java+MySQL的无人停车场管理系统。该系统具有以下特点:数字化、自动化地管理停车场资源;采用先离场后付费的缴费模式,减少用户等待时间;提供无牌车报备功能,解决无法识别无牌车问题。本文详细介绍了该系统的功能设计与实现,并通过测试验证了该系统的可行性和有效性。

基于OpenCV和LSD的车牌识别系统研究

传统车牌识别系统存在图像清晰度不够、识别时间长等问题,有时无法成功识别车牌。为解决这一问题,文章采用OpenCV开放源代码计算机视觉库和线段检测器(Line Segment Detector,LSD)设计了一套新型车牌识别系统。首先,对图像进行灰度化预处理,并使用LSD算法快速检测图像中的直线。其次,采用轮廓跟踪方法识别车牌轮廓,并使用基于垂直投影的分割方法来分割车牌字符。最后,采用基于模板匹配的方法识别车牌号码。经过验证,该系统在处理车牌图片模糊和光线较暗的情况下依然能够高效处理,并且能够保持较高的成功率。

一种基于车牌特征信息的车牌识别方法

提出一种基于车牌特征信息分析的车牌识别方法,它充分利用车牌定位和字符分割过程中得到的信息对车牌识别过程进行反馈,将二值化、车牌定位和字符分割紧密结合,注重车牌与车辆背景图像分离特征,以连通域分析为字符分割特点,结合局部二值化算法,提高正确率.实际应用结果表明,本方法具有很强的环境适应性和鲁棒性.

基于边缘颜色对的车牌定位新方法

车牌定位是车牌自动识别系统中的一个关键问题 .该文提出了一种新的基于边缘颜色对的车牌定位方法 .首先进行彩色边缘检测 ,然后以每一边缘点为中心 ,垂直于边缘方向取一线形窗口 ,在窗口内检测边缘点两侧像素的颜色是否分别匹配车牌的底色与字符颜色 ,若是 ,则保留为候选车牌边缘点 ;然后进行形态滤波 ,剥离不符合车牌结构特征的区域 ,最后对候选车牌区域进行纹理特征的分析以确定真实车牌区域 .该方法抓住了车牌背景与字符具有固定颜色搭配的重要特点 ,综合利用了车牌的结构特征和纹理特征 ,提高了车牌定位的可靠性 .对各种条件下拍摄的 16 3幅含有车牌的图像应用该算法 ,定位准确率达到 98.2 % .