基于深度学习与微服务的车牌识别系统设计与实现

随着城市化进程的加快，停车难问题日益凸显，智慧停车系统作为解决这一问题的有效手段，正受到越来越多的关注。本文详细阐述了智慧停车系统的构建过程，包括软件平台的开发与停车场智能化改造的实践。通过对软件平台核心功能的需求分析、软件架构的设计与技术选型，以及停车场资源的评估与改造策略的制定，实现了停车场的智能化管理。同时，本文还探讨了智慧停车系统软件开发中的用户界面设计、数据管理与分析系统的构建等关键环节，以及系统实施与管理过程中的部署调试、运营维护等策略。最后，对智慧停车系统的未来展望进行了阐述，包括技术创新与系统升级方向，以及智慧停车与城市交通生态的融合趋势。

… The system’s layered, microservices-based architecture ensures … our license plate detection and tracking system lies in two core areas of deep learning: Convolutional Neural Networks (…

Vehicle tracking and license plate recognition (LPR) over video surveillance cameras are essential intelligent traffic monitoring systems. Due to the enormous amount of data collected each day, it would be difficult to track vehicles by license plate in a real-world traffic setting. Large volumes of data processing, real-time request responses, and emergency scenario response may not be possible using conventional approaches. By combining license plate recognition with the docker container-based structure of the Apache Kafka node ecosystem, the suggested solution takes a novel approach to vehicle tracking. The primary components of our suggested framework for reading license plates are the identification of license plates and text data queries. License plate localization is performed with You Only Look Once version 3 (YOLOv3) and character recognition with Optical Character Recognition (OCR). The detected vehicle images with license plate results are published on related topics with Apache Kafka. Apache Kafka is a publish-subscribe (producers-consumers) messaging system and one of the most popular architectures used for streaming data. For each license plate search, a topic will be created in the framework where producers publish and consumers receive data. Thus, the workload of the operators will be reduced and they will be able to pay attention to more important events in traffic.

The UFC Smart Campus project applies IoT and computer vision technologies to enhance campus security through an advanced multi-layered image processing system. Using computer vision and parallel computing, the research develops innovative applications for license plate recognition and bus stop monitoring, achieving high accuracy rates. The system demonstrates a scalable approach to intelligent campus infrastructure, processing multiple camera streams simultaneously and highlighting the potential of cutting-edge AI technologies in academic environments.

This paper presents the design and implementation of an automated traffic monitoring and violation reporting system integrating advanced computer vision and machine learning technologies. The system unifies vehicle detection based on convolutional neural networks, optical character recognition for license plate reading, and real-time speed estimation into a scalable, efficient, and autonomous platform. The architecture incorporates multiple image preprocessing steps to enhance recognition accuracy and employs a multi-threaded design for efficient parallel processing of video streams. A key innovation is the homography estimation of camera parameters to adapt to camera motion, supported by a top-down projection method for precise distance and speed estimation. Reliable communication and automated violation reporting are achieved through a distributed messaging protocol that ensures scalable data transfer and automatic alert delivery to authorities. The comprehensive integration of these components into a single cohesive system reduces complexity and enhances maintainability, providing an effective solution for intelligent traffic surveillance and enforcement within modern transportation infrastructure.

The recent advancements in AI and cloud computing have moved the deployment of high-performance AI models from on-premise infrastructure to cloud-native infrastructure. Even though on-premise infrastructure is cost-effective, the cloud-native infrastructure helps develop scalable and fault-tolerant applications. Therefore, it is very important to choose the right architecture for the deployment based on the need of the application to have a scalable and cost-effective solution. The goal of this research is to study the challenges of deploying high-performance AI models in cloud environments and explore different deployment architectures. Automatic Number Plate Recognition (ANPR) is chosen as the problem for the experiment since ANPR is a high-performance system that has high concurrent uses across multiple organizations.

Cloud-based License Plate Recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You Only Look Once) object detection models, namely YOLO 5, 7, 8 and 9, applied to LPR tasks in a cloud computing environment. Using live video, We performed experiments on YOLOv5, YOLOv7, YOLOv8, and YOLOv9 models to detect number plates in real-time. According to the results, YOLOv8 reported the most effective model for real-world deployment due to its strong cloud performance. It achieved an accuracy of 78\% during cloud testing, while YOLOv5 showed consistent performance with 71\%. YOLOv7 performed poorly in cloud testing (52\%), indicating potential issues, while YOLOv9 reported 70\% accuracy. This tight alignment of results shows consistent, although modest, performance across scenarios. The findings highlight the evolution of the YOLO architecture and its impact on enhancing LPR accuracy and processing efficiency. The results provide valuable insights into selecting the most appropriate YOLO model for cloud-based LPR systems, balancing the trade-offs between real-time performance and detection precision. This research contributes to advancing the field of intelligent transportation systems by offering a detailed comparison that can guide future implementations and optimisations of LPR systems in cloud environments.

Cloud-based license plate recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You Only Look Once) object detection models, namely, YOLO 5, 7, 8, and 9, applied to LPR tasks in a cloud computing environment. Using live video, we performed experiments on YOLOv5, YOLOv7, YOLOv8, and YOLOv9 models to detect number plates in real time. According to the results, YOLOv8 is reported the most effective model for real-world deployment due to its strong cloud performance. It achieved an accuracy of 78% during cloud testing, while YOLOv5 showed consistent performance with 71%. YOLOv7 performed poorly in cloud testing (52%), indicating potential issues, while YOLOv9 reported 70% accuracy. This tight alignment of results shows consistent, although modest, performance across scenarios. The findings highlight the evolution of the YOLO architecture and its impact on enhancing LPR accuracy and processing efficiency. The results provide valuable insights into selecting the most appropriate YOLO model for cloud-based LPR systems, balancing the trade-offs between real-time performance and detection precision. This research contributes to advancing the field of intelligent transportation systems by offering a detailed comparison that can guide future implementations and optimizations of LPR systems in cloud environments.

With the recent and unprecedented increase in demand for Cloud services, furtherly promoted by 5G, Edge computing is emerging as an indispensable technology. Tailored to mitigate the continuously growing load on Cloud data centers and cope with the rising proliferation of IoT (Internet of Things), Single Board Computers (SBCs), embedded systems, and microservices-based applications, edge computing is turning into an integral technology enabler in 5G. Arguably, most of edge microservices will be deployed using virtualization, and specifically using containers instead of VMs (Virtual Machines). Dubbed 5G-MEC (Multi-Access Edge Computing), the 5G edge has to cope with 3 major services: eMBB (enhanced Mobile Broadband), mMTC (Massive Machine Type Communication), and URLLC (Ultra Reliable Low Latency Communication). In this paper, we shed further light on the fundamentals of cloud edge computing and present the subtleties of deploying a real-world SBC-based distributed edge application. The latter is an AI-based application, embedding an image recognition microservice running in containers, deployed in Raspberry PI SBCs, and orchestrated using Kubernetes.

A study was carried out in the field of building a flexible and scalable architecture with support for fast vertical and horizontal scaling. System was developed using microservice architecture. A list of requirements for building an architecture has been developed. Key services and tools for architecture deployment are described. Proposed system allows to detect a car in a traffic flow, to recognize license plate on different angles, to track cars, that are in the in the field of view of cameras. System stores all received information about cars and their tracks and provides fast access to stored data for making some statistical calculations.

… the result of license plate detection using our CNN model. … The distributed computing aspect is also a part of the system, … Modeling deployment on the distributed computing network …

The abstract presents a research study focusing on real-time license plate verification, a key feature of electronic systems that operate by rapidly identifying and removing identification numbers from vehicle registration in a dynamic global environment. The research leverages the combination of artificial intelligence (AI) and machine learning (ML) techniques, specifically the integration of region-based convolutional neural networks (RCNN) and advanced RCNN algorithms, to create a powerful and readily available system. In terms of methods, this research optimizes algorithm performance and deploys the system in a cloud-based environment to improve accessibility and scalability. Through careful design and optimization, the proposed system has achieved a consistent result in license recognition, as evident from the well-accounted evaluation of performance, including precision, recall, and computational efficiency. The results demonstrate the efficiency and usability of this system in a real installation and promise to revolutionize automatic vehicle identification. Finally, the integration of artificial intelligence and machine learning technology into real-time license plate recognition signifies changes in traffic management, assessment safety and smart city plans. Therefore, interdisciplinary collaboration and continuous innovation are crucial to shaping a sustainable and balanced future for intelligent transportation systems.

Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system. The system is based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates. The information redundancy of Saudi license plates’ Arabic and English characters is leveraged to boost the license plate recognition accuracy while satisfying real-time inference performance. The system optimally achieves real-time performance on edge GPU devices and maximizes models’ accuracy by taking advantage of the temporally redundant information of the video stream’s frames. The edge device sends a notification of the detected vehicle and its license plate only once to the cloud after completing the processing. The system was experimentally evaluated on vehicles and license plates in real-world unconstrained environments at several parking entrance gates. It achieves 17.1 FPS on a Jetson Xavier AGX edge device with no delay. The comparison between the accuracy on the videos and on static images extracted from them shows that the processing of video streams using this proposed system enhances the relative accuracy of the car model and license plate recognition by 13% and 40%, respectively. This research work has won two awards in 2021 and 2022.

… , an IoT based cloud integrated system is deployed to ease the parking system of a university … is a non-linear filter with weights that are Gaussian distributed [12]. These weights take into …

Innovative solutions in transportation management, have been made possible by the quick development of computer vision technologies. This project presents the Automated Toll Booth System (ATBS) through the integration of YOLOv8 (an object detection algorithm) and the Own build ATBS API for accurate number plate recognition to streamline toll collection procedures. In order for the suggested system to function, YOLOv8 must be deployed in order to identify incoming vehicles and extract the zones of interest that include number plates. The ATBS API is then used to process these areas, extracting alphanumeric letters from the number plates through the use of optical character recognition (OCR) technologies. To ensure smooth toll collection, the retrieved plate numbers are then compared to a database of registered cars. The ATBS’s high precision number plate recognition, flexibility to handle different traffic volumes, and real-time processing capabilities are some of its key advantages. Additionally, the system is made to require as little manual intervention as possible, which lowers operating costs and increases efficiency. The efficacy of the suggested ATBS is confirmed by extensive testing and performance analysis, indicating its potential to transform toll collection systems by providing a dependable, automated solution. This study adds to the continuing endeavors to improve traffic management systems and transportation infrastructure through the application of computer vision technologies.