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.

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.

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.

Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data

Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain extent.However,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure.To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective characteristics.Subsequently,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online.The experimental results show that PURP retains strong prediction efficiency as the prediction period increases.

Traffic control optimization strategy based on license plate recognition data

Traffic signal control is essential to the efficiency of the road network’s operation.In recent years,more and more detailed detection data provide potential data support for traffic signal control,such as license plate recognition(LPR)data.This study aims to develop a traffic signal control optimization method based on model predictive control(MPC)and LPR data.The proposed framework of a closed-loop control system is described in detail.First,the control objectives and queue prediction model for signalized intersection are determined.Then,online optimization and feedback compensation are discussed and implemented.Calculations of the arrival rate at the downstream are based on the LPR data detected at the upstream intersection,and dynamic optimization method of the offset is proposed for a coordinated control.The model is validated using the LPR data of two consecutive intersections with a traffic simulation platform.Results demonstrate that the model can restrain extreme long queuing,improve intersection capacity,and reduce intersection average delay.The developed model promotes the system operating efficiency and shows the general advantage of real-time optimization,feedback,and control.The proposed framework can be potentially applied by local traffic management centers to improve the quality of traffic signal control.

复杂道路环境下大流量路口多车牌自动识别

复杂道路口车流量大,车牌采集图像存在遮挡,倾斜等现象,导致车牌检测准确率低、泛化性差。为解决上述问题,提出一种基于改进Yolov3车辆识别与Mask R-CNN车牌检测相融合的算法,通过两步分别检测车辆与车牌,最终构建出YOL3-RCNN多车牌识别模型。模型首先基于上采样算法将图像的大、中、小特征有机融合;然后采用形态聚类算法,自适应计算IOU阈值,提高车辆检出率与准确率;接着对检测出的车辆进行裁剪,SOFTNMS优化与仿射变换,解决车牌遮挡,倾斜与背景复杂的难题;最后基于ResNet50网络识别车牌字符与数字,并利用RPN进行回归校验。多组基线算法的仿真结果显示,在UA-DETRAC车辆检测数据集上,与传统车牌检测算法相比,YOL3-RCNN算法的准确率与召回率分别提高了7.7%与9.6%,且具有较高的车牌检测速率。综上所述,所构建的YOL3-RCNN多车牌识别模型具有较高的检测准确性、时效性与普适性。

基于深度学习的非约束场景多车牌识别方法研究

为解决非约束场景下的车牌识别精度问题,提出一种能够适应非约束场景并识别不同类型单车牌以及多车牌的自动车牌识别模型。该模型应用数据迁移技术,通过YOLOv5检测车辆并通过后处理筛选有效车辆目标,经检测并矫正后,通过ResNet18以及双向长短期记忆网络BLSTM网络结合连接时序分类损失CTC识别车牌字符。在模型训练过程中使用数据增强技术进一步提高了模型性能。该模型在CCPD以及AOLP的多个子数据集上进行了测试,展现出了优于其它方法识别精度和识别速度。

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。

基于车牌识别的卡口数据质量监测方法研究及应用

随着汽车保有量的增长,为了提高城市道路交通管理水平,越来越多的城市在道路上安装了电子监控设备,产生了海量的基于车牌识别的卡口数据,并被广泛应用到不同的领域。卡口数据的应用离不开对数据质量、数据实时性的要求,探讨了一套可以实时监测卡口数据质量的指标方法。该方法首先确定卡口设备数据监测的内容,其次是建立数据监测的计算指标,最后通过实际系统给出该方法的应用流程及示例。该方法已被应用到宣城、上海等城市大脑系统的数据监测模块,以五分钟的粒度计算指标,能够及时发现上报出现质量问题的卡口设备,避免因卡口数据质量问题而影响后续应用。

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.

汽车牌照快速定位算法

汽车牌照的快速准确定位是汽车牌照实时自动识别技术中非常关键的一步。本文利用汽车牌照尺寸、字间距、字数和字体统一的纹理特征 ,提出了基于边缘检测和投影法相结合的牌照定位算法和基于二值化和投影法相结合的牌照定位算法。大量实验结果表明 ,两种算法都具有快速、简洁实用和跟图象背景的复杂程度相关性小的特点。

高性能的车牌识别系统(英文)

描述了一个车辆牌照识别系统 .该系统首先利用车辆位置传感器和图像采集卡来自动获取车辆图像并传输至计算机 ,然后识别车牌字符 .结合网络技术 ,特定车牌信息和车辆图像可以很方便地从远端检索到 .文中介绍了该系统的结构及工作流程 ,以及两种字符的识别方法 :基于PCA LSM的有限中文字符识别方法和基于结构特征分析的字母及数字字符识别方法 .在实际应用环境下 ,该系统的日间整体识别率超过 97% ,夜间整体识别率超过 95 %

基于网格化监控的套牌车检测系统

针对目前套牌车泛滥的现状,提出了一种基于网格化监控的套牌车检测系统。该系统使用车牌识别技术,采集经过监测点车辆的信息,如车牌、出现时间。按照一辆车不可能"同时"出现在两个地点的原理,自动完成套牌检测。在车牌识别技术可靠的基础上,该系统的套牌检测率不低于99%。

基于PSO-BP神经网络的车牌号码识别技术

针对传统BP神经网络在车牌识别的应用领域中,存在经常性陷入局部最优,而导致识别效果不理想事实。提出一种基于PSO-BP神经网络的车牌号码识别技术方法,该方法首先构建一个8-25-1的BP神经网络,再通过提取车牌的8像素比特征值作为BP神经网络的输入向量,然后采用粒子群算法(PSO)对该BP神经网络的权值和阈值进行优化,使其适应值达到最小。通过300副不同光照环境和污损的车牌识别仿真实验,验证了所提出的算法相对于传统的模板匹配算法和BP算法,具有输出误差小、全局收敛速度快和识别率高的特征,具有一定的应用价值。

基于线圈与车辆号牌识别技术的治安卡口系统

This paper introduces the Security System based on loops and license plate recognition.With an in-depth discussion about the key technology in the vehicle detection and the video adjustment,the paper presents a methodof obtaining a target video of good quality in all weather.The practices show that the proposed system is an effective public security measure at the checkpost.

字符多特征提取方法及其在车牌识别中的应用

针对车牌字符识别中大部分单一特征提取方法在字符识别上的局限性,提出了一种车牌字符多特征提取方法。在经过预处理后的车牌细化字符基础上提取字符4个侧面的笔画特征、拐点特征、轮廓累积特征及字符内部像素特征,构建出一个维度较低的特征向量集,然后分别采用支持向量机、K近邻算法、BP神经网络、径向基神经网络对陆丰高速公路实地拍摄的车牌图片进行测试并分别与模板匹配方法、网格法、基于小波矩方法比较,实验结果表明提出的车牌字符多特征提取方法识别率高,鲁棒性好。

ETC电子不停车收费的技术研究

在国内首次提出并实现了一种基于多技术融合、分布式处理的ETC电子不停车收费系统。系统采用射频识别、模式识别、数据安全与加密、信息融合、计算机网络等多种技术的融合,采用分布式处理的方式最大限度地保证了系统的实时性、可靠性和安全性。系统依据中国国情,对所运用的各项技术进行了深入的研究,并通过在河南濮阳的实地试验验证了所采用技术的有效性。

小区停车场智能管理系统设计

针对小区停车场使用和管理的特点,设计了高效实用的停车场智能管理系统方案。该方案将射频识别、IC卡管理、车辆自动监测及计算机智能控制技术有机地融合于一体,能够根据业主和客户的实际需要,提供最合适的系统构架,具有先进、可靠、安全、方便、快捷和可扩充等特点。同时,对其中的关键技术,车牌自动识别做了详细讨论。

基于车牌照数据的通勤特征车辆识别研究

近几年来,交通拥堵日益成为大中城市最严重的交通问题之一,而由通勤行为引起的早晚高峰交通拥堵最为突出,严重影响了城市居民的出行和交通系统的运行.因此,本文从车辆的角度出发,采用上海快速路牌照识别系统采集数据,通过k-means聚类数据挖掘方法,提取路网中的通勤特征车辆,并分析了通勤特征车辆在路网中的出行时空分布.分析得出,在上海快速路网中,占全部检测车辆2.8%的通勤特征车辆在早晚高峰时提供了高达36%的交通量.在早高峰时段,识别出的通勤特征车辆主要分布路段为中环外圈、陆家嘴方向、逸仙高架和沪闵高架;晚高峰时段,基本集中在与早高峰的相反方向.研究结果表明,本文基于车牌照数据的数据挖掘方法,可以有效地提取通勤特征车辆并研究其出行行为的时空特征,能够为城市交通拥堵问题的缓解和交通需求管理政策的提出提供辅助决策信息.