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.

Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain technology.The Internet of Things(IoT)is a network of connected,interconnected gadgets.Privacy issues occasionally arise due to the amount of data generated.However,they have been primarily addressed by blockchain and smart contract technology.While there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT environment.This study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license plates.While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research attention.Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source libraries.This method works reasonably well for identifying any license plate.The Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various datasets.First,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 films.Second,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset.Third,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting correctly.The proposed model,however,produced beneficial outcomes.

License Plate Recognition via Attention Mechanism

License plate recognition technology use widely in intelligent trafficmanagement and control. Researchers have been committed to improving thespeed and accuracy of license plate recognition for nearly 30 years. This paperis the first to propose combining the attention mechanism with YOLO-v5and LPRnet to construct a new license plate recognition model (LPR-CBAMNet).Through the attention mechanism CBAM(Convolutional Block AttentionModule), the importance of different feature channels in license platerecognition can be re-calibrated to obtain proper attention to features. Forceinformation to achieve the purpose of improving recognition speed andaccuracy. Experimental results show that the model construction methodis superior in speed and accuracy to traditional license plate recognitionalgorithms. The accuracy of the recognition model of the CBAM model isincreased by two percentage points to 97.2%, and the size of the constructedmodel is only 1.8 M, which can meet the requirements of real-time executionof embedded low-power devices. The codes for training and evaluating LPRCBAM-Net are available under the open-source MIT License at: https://github.com/To2rk/LPR-CBAM-Net.

网格环境中软件共享系统的License管理器

通过分析现有软件License管理系统存在的不足,提出了一种网格环境中软件License组织管理方式,并设计和实现了软件共享系统的License管理器.该License管理器具有独立于各种LMS系统、License获取效率高、支持License资源预留等优点,能够满足网格环境中对License资源管理多样性和动态性的要求,避免了以往用户随机获取license资源的盲目性.针对用户不同优先级提出了确保不同服务质量的调度策略,License调度时采取“装填算法”并结合预留机制.通过测试表明,系统可以加快任务的完成时间,并提高了资源利用率.

网格环境中基于浮动License的软件资源共享

传统基于License的管理系统可在局域网范围内实现一定的软件共享,但无法适用于开放、异构和动态的网格环境。本文提出一种基于浮动License机制的新型全局软件共享系统LicTraveler,在不改变原有License使用机制的前提下实现License的全局管理和跨域共享。两层体系结构可同时提供自治局部License服务和协同全局License服务;Li-cense的预留与配额策略、布局与调度策略为用户提供了灵活性和健壮性。实验表明,LicTraveler能够有效降低软件的重复投资,可将License的使用效率提升50%以上。

企业级软件License控制系统的研究

在传统的License加密方法基础上把新兴的多步加密算法和传统的非对称RSA加密算法进行结合,构建了一个企业级的软件License控制系统。开发了一个企业级License控制系统来检验此加密算法,并阐述了实现过程。

校园网格环境下软件License调度算法及策略研究

应用软件License是昂贵的计算资源,校园网格是分布、共享的应用环境,在该环境下研究License的管理有着重要的意义。本文提出了支持License管理策略的校园网格管理系统体系结构,提出了基于角色和等待时间的网格作业动态优先级计算方法,提出了基于资源稀缺性和负载的License预留策略及剥夺策略,并在此基础上提出了基于动态优先级和集群负载的网格资源调度算法PLS。提出的算法和策略在校园网格进行了部署和应用,证明了其可以提高资源利用率,实现不同等级用户服务的差异性,保证同等级用户之间的公平性。

关于PACS/RIS系统中License权限管理的探讨

目的解决License权限并行数量不足,导致医生在报告工作站无法从PACS服务器调阅影像信息的问题。方法在不扩增License权限数量的前提下,将负责图像调阅的Llicense权限服务从现有的PACS服务器转移到一台单独的服务器上,通过相应的参数配置和软件调试,再将此服务器与现有的医生报告工作站连接。结果解决了License权限并行数量不足的瓶颈问题。结论 License权限并行问题的解决使PACS/RIS的运行更加稳定,极大地提高了医生的工作效率。

基于license文件的软件加密技术的研究与应用

本文是在软件盗版问题日益严重的背景下,提出一种防盗版软件加密方法,其基于license文件的软件加密技术。该技术包括两个方面:license文件的管理技术和通信系统的加密技术。通过对这两方面技术进行分析和研究,提出了相应的解决方法,并对这一软件加密技术进行验证和应用。

Super-resolution reconstruction for license plate images of moving vehicles

A novel reconstruction method to improve the recognition of license plate texts of moving vehicles in real traffic videos is proposed, which fuses complimentary information among low resolution （LR） images to yield a high resolution （HR） image. Based on the regularization super-resolution （SR） reconstruction schemes, this paper first introduces a residual gradient （RG） term as a new regularization term to improve the quality of the reconstructed image. Moreover, L1 norm is used to measure the residual data （RD） term and the RG term in order to improve the robustness of the proposed method. Finally, the steepest descent method is exploited to solve the energy functional. Simulated and real acquired video sequence experiments show the effectiveness and practicability of the proposed method and demonstrate its superiority over the bi-cubic interpolation and discontinuity adaptive Markov random field （DAMRF） SR method in both signal to noise ratios （SNR） and visual effects.

虚拟专用网License管理的一种改进方案

虚拟专用网VPN拥有大量的用户群,其用户数量的License功能管理也显得非常重要。针对虚拟专用网用户数的管理存在缺陷的现状,提出了一种改进的License管理方案。增加一个是否支持公司License控制方案的开关来兼容原有功能;由SMP统一获取所有SCP用户数,并对所有SCP进行统一控制;对servicenumber表和mvpn_servicenumber表用户数量进行加密修改,确保用户数不被非法用户盗取;同时提供了一条MML命令来激活License文件,有效解决从License文件读取用户配置项的数据。最后,分析比较了当前改进方案的优点。

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.

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.

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.

基于实时授权的License控制技术在制造执行系统中的应用

针对加工制造行业MES(Manufacturing Execution System,制造执行系统)的软件版权控制、并发用户数控制、软件版本(试用版、正式版、软件租赁)控制等问题,本文研究了一种基于实时授权的企业级软件License控制方法,并将该方法应用在采用面向服务架构(SOA—service oriented architecture)的MES中。本文将就其中的关键模块--License控制模块进行详细阐述,并对系统的实现和应用效果进行介绍。

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 Plate Recognition Using Optimal Deep Learning Model

The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the vehicles.The VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing images.The current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN model.The presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and recognition.Hough Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP.The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters.The HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge datasets.The experimentation outcome verified that the presented method was better under several aspects.The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.

Saudi License Plate Recognition Algorithm Based on Support Vector Machine

-License plate recognition （LPR） is an image processing technology that is used to identify vehicles by their license plates. This paper presents a license plate recognition algorithm for Saudi car plates based on the support vector machine （SVM） algorithm. The new algorithm is efficient in recognizing the vehicles from the Arabic part of the plate. The performance of the system has been investigated and analyzed. The recognition accuracy of the algorithm is about 93.3%.