License Plate Research Articles

Macroscopic fundamental diagram with volume–delay relationship: Model derivation, empirical validation and invariance property

This paper presents a macroscopic fundamental diagram model with volume–delay relationship (MFD-VD) for road traffic networks, by exploring two new data sources: license plate cameras (LPCs) and road congestion indices (RCIs). We derive a first-order, nonlinear and implicit ordinary differential equation involving the network accumulation (the volume) and average congestion index (the delay), and use empirical data from a 266 km2 urban network to fit an accumulation-based MFD with R2>0.9. The issue of incomplete traffic volume observed by the LPCs is addressed with a theoretical derivation of the observability-invariant property: The ratio of traffic volume to the critical value (corresponding to the peak of the MFD) is independent of the (unknown) proportion of those detected vehicles. Conditions for such a property to hold are discussed in theory and verified empirically. This offers a practical way to estimate the ratio-to-critical-value, which is an important indicator of network saturation and efficiency, by simply working with a finite set of LPCs. The significance of our work is the introduction of two new data sources widely available to study empirical MFDs, as well as the removal of the assumptions of full observability, known detection rates, and spatially uniform sensors, which are typically required in conventional approaches based on loop detector and floating car data.

Шляхи оптимізації кількості індивідуального транспорту в обмежених можливостях вулично-дорожньої мережі

An analysis of scientific research was conducted in the direction of reducing the negative impact of the use of individual vehicles on the street and road network of cities. In order to solve the problem of transport oversaturation of public transportation systems, the foreign experience of limiting the use of individual vehicles on the street and road network of cities was analyzed. The main directions of restrictions on the use of individual vehicles on the street and road network of cities have been established, including restrictions on the operation of individual vehicles depending on the number of passengers in the cabin, the digital configuration of the state license plate, and the presence of vehicle certification. Methods of limiting the operation of individual vehicles in the busy part of the city were also considered. These measures are aimed at reducing the intensity of traffic in cities and increasing the filling factor of the vehicle interior. Considering that a large share of individual vehicles used in Ukraine belongs to Euro-2 and Euro-4 environmental standards, their use significantly worsens the environmental situation in large cities. Approaches aimed at increasing the use of public transport and other alternative modes of transport for transporting passengers in cities are summarized, which reduces the load on the street and road network and improves the environmental situation. To solve the described problems, it is necessary to use a comprehensive approach, in particular, to apply measures of both a regulatory and an informational nature, primarily to popularize the use of public transport. The proposed measures to solve the described transport problems will improve the comfort of transport connections for all road users and the environmental situation, especially in large cities of Ukraine.

Smart Traffic Management: Enhancing Urban Mobility through Predictive Analysis and AI-Driven Solutions

In urban environments, the issue of unauthorized parking in designated no-parking zones persists, leading to traffic congestion and safety hazards. inaccurate license plate recognition, License plate (LP) detection is a crucial task for Automatic License Plate Recognition (ALPR) systems. Most existing LP detection networks can detect License plates, but their accuracy suffers when license plates (LPs) are tilted or deformed due to perspective distortion. This leading to difficulties in identifying vehicle owners. To address this challenge, this project present TraceMe, a predictive system utilizing advanced machine learning algorithms. The system employs YOLOv8 for efficient object detection, focusing on identifying vehicles in no-parking zones, and Tesseract OCR for accurate license plate recognition. The extracted license plate information is then processed by a machine learning model trained to predict the owner of the vehicle. The proposed system involves collecting and annotating a diverse dataset, training YOLOv8 and LPRNet model for vehicle number plate detection, utilizing Tesseract OCR for license plate extraction, and implementing a machine learning model for owner identification. Real-time processing and integration with surveillance systems allow for immediate identification of unauthorized parking incidents. The system generates alerts or notifications, aiding law enforcement in enforcing parking regulations.TraceMe not only provides a technological solution to mitigate unauthorized parking but also contributes to improved traffic management and public safety.

DiffPlate: A Diffusion Model for Super-Resolution of License Plate Images

License plate recognition is a pivotal challenge in surveillance applications, predominantly due to the low resolution and diminutive size of license plates, which impairs recognition accuracy. The advent of AI-based super-resolution techniques offers a promising avenue to ameliorate the resolution of such images. Despite the deployment of various super-resolution methodologies, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), the quest for satisfactory outcomes in license plate image enhancement persists. This paper introduces “DiffPlate”, a novel Diffusion Model specifically tailored for license plate super-resolution. Leveraging the unprecedented capabilities of Diffusion Models in image generation, DiffPlate is meticulously trained on a dataset comprising low-resolution and high-resolution pairs of Saudi license plates, curated for our surveillance application. Our empirical analysis substantiates that DiffPlate markedly eclipses state-of-the-art alternatives such as SwinIR and ESRGAN, evidencing a 26.47% and 37.32% enhancement in Peak Signal-to-Noise Ratio (PSNR) against these benchmarks, respectively. Furthermore, DiffPlate achieves superior performance in terms of Structural Similarity Index (SSIM), with a 4.88% and 16.21% improvement over SwinIR and ESRGAN, respectively. Human evaluative studies further corroborate that images refined by DiffPlate were preferred 92% more frequently compared to those processed by other algorithms. Through DiffPlate, we present a new solution to the license plate super-resolution challenge, demonstrating significant potential for adoption in real-world surveillance systems.

Efficient Vehicle Registration Recognition System: Enhancing Accuracy and Power Efficiency through Digital Image Processing

Number-plate recognition technology uses optical character recognition on images to read vehicle registration plates using OpenCV, PyTesseract OCR Engine, and YOLOv4 model. Images of the license plates are provided to the YOLOv4 model using a web app. The model is trained with the help of images from the Kaggle dataset. Then the number plate is extracted from the vehicle using the model. The string from the image is extracted using PyTesseract OCR Engine. Tested on several datasets this method gave us a success rate of 94%. This method can also be used in real-time incidents.

An IoT vehicle management framework based on optimized license plate recognition

With the continuous development of urban traffic and the increasing number of vehicles, vehicle management has become increasingly crucial. The Harmony-based IoT vehicle management system, leveraging Internet of Things (IoT) technology, offers an effective solution. This paper introduces the concept and principles of the Harmony-based IoT vehicle management system, focusing on optimized license plate recognition. Firstly, the principles and optimization methods of license plate recognition technology are elaborately described, encompassing aspects such as image processing, feature extraction, and pattern recognition. Subsequently, the paper explores the application of advanced YOLOv7 license plate recognition technology in the Harmony-based IoT vehicle management system, incorporating discussions on interlinking with devices like gate control systems.

Quantifying the effect of driving restrictions on fine particulate matter concentrations with WRF-Chem model: A case study in Kaifeng, China

One of the significant sources of urban air particulate matter (PM2.5 and PM10) has been found to be vehicle exhaust emission. Vehicle restriction measures have been taken to alleviate particulate matter pollution in the world. Here, we quantified the impact of vehicle restriction measures on the PM2.5 concentrations using the Weather Research and Forecasting model with Chemistry (WRF-Chem) in Kaifeng, China. This study set up three different scenarios to simulate the spatiotemporal changes of PM2.5 concentrations in four seasons in 2020. They were no emission reduction (S0), the restriction of two tailing license plate numbers (S1), and the restriction of odd–even tailing numbers of license plates (S2), respectively. In general, the odd–even license plate restriction policy has proven to be more effective than restricting two specific license plate numbers. In scenario S1, the range of PM2.5 concentrations reduction in Kaifeng City was between 0.2% and 1.28%. In scenario S2, the range of PM2.5 concentrations reduction was between 0.98% and 14.42%. The results showed that the effectiveness of vehicle restriction measures varies from season to season, and the effect in summer and winter is better than that in spring and autumn. We suggest that it may be difficult to reduce the PM2.5 concentrations to a great extent if simply reducing the traffic exhaust emissions through motor vehicle restrictions, on the contrary, would lead to the increase of O3 concentration, bringing some other effects. This study can serve as a foundation for the rational development of motor vehicle control strategies and help advance the ongoing improvement of air quality.

Evaluating Factors Shaping Real-Time Internet-of-Things-Based License Plate Recognition Using Single-Board Computer Technology

Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam’s imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system’s reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data’s precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations.

DEEP AND MACHINE LEARNING MODELS FOR RECOGNIZING STATIC AND DYNAMIC GESTURES OF THE KAZAKH ALPHABET

Currently, an increasing amount of research is directed towards solving tasks using computer vision libraries and artificial intelligence tools. Most common are the solutions and approaches utilizing machine and deep learning models of artificial neural networks for recognizing gestures of the Kazakh sign language based on supervised learning methods and deep learning for processing sequential data. The research object is the Kazakh sign language alphabet aimed at facilitating communication for individuals with limited abilities. The research subject comprises machine learning methods and models of artificial neural networks and deep learning for gesture classification and recognition. The research areas encompass Machine Learning, Deep Learning, Neural Networks, and Computer Vision. The main challenge lies in recognizing dynamic hand gestures. In the Kazakh sign language alphabet, there are 42 letters, with 12 of them being dynamic. Processing, capturing, and recognizing gestures in motion, particularly in dynamics, pose a highly complex task. It is imperative to employ modern technologies and unconventional approaches by combining various recognition methods/algorithms to develop and construct a hybrid neural network model for gesture recognition. Gesture recognition is a classification task, which is one of the directions of pattern recognition. The fundamental basis of recognition is the theory of pattern recognition. The paper discusses pattern recognition systems, the environment and application areas of these systems, and the requirements for their development and improvement. It presents tasks such as license plate recognition, facial recognition, and gesture recognition. The field of computer vision in image recognition, specifically hand gestures, is also addressed. The development of software will enable the testing of the trained model's effectiveness and its application for laboratory purposes, allowing for adjustments to improve the model.

Rainfall impacts on urban route choices by private vehicle users: insights from São Paulo, Brazil

Urban drivers frequently experience challenges posed by adverse weather conditions like heavy rain. Despite the influence of these conditions on individual travel behaviour, there is limited understanding of how various aspects of rainfall affect the route choices in urban trips. In this context, this paper aims to evaluate the impacts of different rainfall conditions on the route choice behaviour of private vehicle users. We propose a method to identify the specific routes taken by drivers, utilizing observed trips reproduced from a large-scale dataset containing license plate information recognized by speed device cameras. Path Size Multinomial Logit models penalizing routes that share common links were estimated based on a choice set comprised of routes with a maximum similarity threshold. Distance and the actual travel times in the routes were estimated using real-world data from a third-party company, and cumulative, average and maximum rainfall through each route of the choice set were obtained from data captured by meteorological radar. A case study in the city of São Paulo, Brazil, considered the average and maximum rainfall intensities, distance, and travel time in different models. The results indicate that both average and maximum rainfall intensities negatively impact the utility of routes. This research enables the identification of routes with the highest probabilities of being chosen during intense rainfall. Such information is valuable for implementing measures to minimize the impacts caused by adverse weather conditions in urban trips.

Modal shift, environmental benefits and population exposure assessment on contingent transport policies

Transport policies targeting emission reduction and air quality improvement typically discourage private car use and promote shifts to mass transit (bus, metro). However, possible increase of travelers’ exposure while adapting to those policies have often been overlooked, which is addressed in this work via a case study of Heavy Pollution Episodes (HPEs) in Chengdu, China, using city-scale traffic camera and transit ridership data. By investigating traffic fluctuation and modal shift under prolonged license plate rationing for private cars and free or discounted fare for mass transit, we show that the reduction of small car traffic was marginal (up to 2.3 % in 24-hour periods), which, environmental benefits wise, is negligible according to air quality modelling. Meanwhile, over 3.23 % of car users switched to mass transit during Orange-level alerts, with a higher degree (by 19 %–39 %) of exposure to PM2.5. Our findings highlight the importance of exposure-centric decision making during HPEs.

Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition.

ГОДОВОЙ ПЛАН-ГРАФИК ТЕХНИЧЕСКОГО ОБСЛУЖИВАНИЯ С ИСПОЛЬЗОВАНИЕМ ПРОГРАММЫ MICROSOFT EXCEL

We reviewed the main stages of the development of the annual schedule of maintenance and technical diagnostics of the fleet of trucks on a computer, taking into account individual information on each of their samples and the time features of the formation of the fleet in a separate farm. (Research purpose) The research purpose is developing an annual schedule of maintenance and technical diagnostics of the fleet of trucks in the MS-Office program. (Materials and methods) It was shown that in order to solve the problem, the necessary information is entered into the computer in specially designed tabular forms. Used as input information: the list of trucks of the farm; the term of their commissioning/overhaul and mileage after it; the annual schedule of vacations of farm drivers; the annual schedule of holidays and weekends; the average annual mileage of farm truck models over the past year; schedules of intensive spring and autumn mechanized transport operations of the farm; individual mileage of all trucks over the past year year; individual planned mileage of each truck for a year; individual mileage of them after the last license plate maintenance. (Results and discussion) The output information is presented in the table for all trucks with the dates of their numbered technical services and technical diagnoses by month of the year. We used the Microsoft Excel spreadsheet package as a software environment for developing an annual maintenance schedule and technical diagnostics, which allows us to implement the principle of single data entry, thereby reducing the complexity of transferring them to a computer. (Conclusions) The development was presented as a special version of the system of software-algorithmic and information tools generated in the MS-Office spreadsheet environment.

The Influence of License Plate Allocation Mechanism on Urban Traffic Management: A Comparative Study of Beijing and Shanghai

Urban traffic management has grown in importance since China’s cities have seen a sharp rise in the number of motor vehicles in recent years. In this context, different cities have launched different license plate allocation mechanisms, among which the license plate auction mechanism in Shanghai and the license plate lottery system in Beijing have a greater impact on urban traffic management. Their roles are undeniable while their drawbacks need to be solved. The license plate allocation system can only alleviate urban road congestion, and it is difficult to fundamentally solve the problem, which also requires the government to continue to study, in order to find a long-term solution to promote the innovative innovation of urban traffic management. This paper focuses on the influence of the Beijing license plate lottery system and Shanghai license plate auction system on urban traffic management, points out the drawbacks of different license plate allocation mechanisms and puts forward suggestions, which can clarify the role of different license plate allocation mechanisms in urban traffic management. In order to improve the license plate allocation mechanism and optimize the development path of the license plate allocation mechanism, it is essential to improve the function mechanism of the license plate allocation mechanism in urban traffic management.

Left-turn queue spillback identification based on single-section license plate recognition data

Left-turn queue spillback occurs when the queue of left-turn or adjacent through traffic exceeds the channelized section's capacity, resulting in discharge flow breakdown and inefficient use of green time. License plate recognition (LPR) systems, which offer lane-based data on individual vehicle discharging, present an ideal data source for the identification of queue spillback. Therefore, this paper proposes a method for left-turn queue spillback identification using single-section LPR data. The proposed method involves defining 10 types of left-turn spillbacks based on the queuing status of the left-turn and adjacent through lanes. Then, a lightweight algorithm is proposed to estimate lane-based queue lengths. At last, a rule-based identification algorithm is developed to specify spillback types by considering the left-turn phasing, estimated queue lengths, and lane capacity. The empirical experiment demonstrates a promising accuracy of 87.28%. Additionally, simulations with five left-turn phasing schemes further validate the proposed method, averaging nearly 90% accuracy in identifying spillbacks.

A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others.

SterAF: A Scene Text Recognizer with Appearance-Flow Rectification

As the demand for recognizing irregular text in natural scenes increases, people are increasingly realizing the value of such applications, such as license plate recognition systems, image search, handwriting recognition, and autonomous driving, which are profoundly changing our lives in the field of text recognition. Recent studies have shown that the recognition of curved text and perspective text has become an important challenge in the field of text recognition, and the correction of curved text is a key step to achieve accurate recognition. However, current methods use strained text image correction methods, resulting in poor recognition accuracy when recognizing curved text. Therefore, we propose an end-to-end framework called Scene Text Recognizer with Appearance-Flow rectification (SterAF), which includes a correction network and a recognition network. Specifically, the framework’s steps are as follows: first, the input text image is deformed through an appearance flow-based correction network to adaptively warp the text image, to prevent irregular and unnatural deformations of the text image. Second, a sequence-to-sequence recognition network predicts the sequence of characters in the corrected text image to accurately recognize the text in the image. Through subjective and objective experiments, our SterAF model has shown excellent performance in both qualitative and quantitative experiments.

Deep learning adversarial attacks and defenses on license plate recognition system

The breakthroughs in Machine learning and deep neural networks have revolutionized the handling of critical practical challenges, achieving state-of-the-art performance in various computer vision tasks. Notably, the application of deep neural networks in optical character recognition (OCR) has significantly enhanced the performance of OCR systems, making them a pivotal preprocessing component in text analysis pipelines for crucial applications such as license plate recognition (LPR) systems, where the efficiency of OCR is paramount. However, despite the advancements, the integration of deep neural networks in OCR introduces inherent security vulnerabilities, particularly susceptibility to adversarial examples. Adversarial examples in LPR systems are crafted by introducing perturbations to original license plate images, which can effectively compromise the integrity of the license plate recognition process, leading to erroneous license plate number identification. Given that the primary goal of OCR in this context is to accurately recognize license plate numbers, even a single misinterpreted character can significantly impact the overall performance of the LPR system. The vulnerability of LPR systems to adversarial attacks underscores the urgent need to address the security weaknesses inherited from deep neural networks. In response to these challenges, the exploration of alternative defense mechanisms, such as image denoising and in-painting, presents a compelling approach to bolstering the resilience of LPR systems against adversarial attacks. By prioritizing practical implementation and integration of image denoising and inpainting techniques align with the operational requirements of real-world LPR systems. These methods can be seamlessly integrated into existing pipelines, offering pragmatic and accessible means of enhancing security without imposing significant computational overhead. By embracing a multi-faceted approach that combines the strengths of traditional image processing techniques, the research endeavors to develop comprehensive and versatile defense strategies tailored to the specific vulnerabilities and requirements of LPR systems. This holistic approach aims to fortify LPR systems against adversarial threats, thereby fostering increased trust and reliability in the deployment of OCR and LPR technologies across various domains and applications.

Enhancing Bangladeshi License Plate Recognition: A YOLOv8 Approach with Roboflow Integration for Accuracy and Speed Optimization

Abstract: Automatic License Plate Recognition (ALPR) systems are indispensable in modern transportation services, offering crucial benefits in traffic management, parking, toll collection, and surveillance. However, implementing ALPR in Bangladesh presents challenges due to the intricacies of Bangla characters and low-resolution CCTV images. Despite ALPR's potential to enhance safety and security, issues such as license plate variations and image quality hinder its effective implementation. This research addresses these challenges by developing a customized YOLOv8 model tailored for recognizing Bengali license plates, with a focus on precise localization, character segmentation, and script deciphering. Leveraging advanced deep learning techniques, the study aims to enhance efficiency and accuracy for applications in law enforcement and traffic management. Through a comprehensive workflow integrating Roboflow and YOLOv8, effective dataset collection, annotation, augmentation, and model training are demonstrated. System evaluation on diverse test datasets confirms the reliable detection and recognition of Bangladeshi license plates, underscoring the model's practical utility and robustness in real-world scenarios. Additionally, the research showcases notable advancements in ALPR technology specific to Bangladesh's unique context. Noteworthy is the model's exceptional performance in detecting license plates from corner angles, even across various vehicle types, as well as its ability to accurately identify plates amid challenging conditions such as broken or obscured plates and low-resolution images. Moreover, the system's proficiency in scenarios with multiple license plates, like those on buses adorned with banners, contributes significantly to improved road safety and traffic management.

Moving Vehicle Number Plate Detection

Abstract: The ability to detect and recognize vehicle license plates is a crucial task in various applications such as traffic management, law enforcement, and parking systems. In this project, we propose a system for moving vehicle license plate detection using image and video processing techniques. The system employs a combination of computer vision algorithms and machine learning models to accurately locate and recognize license plates from moving vehicles in real-time. The input images or video frames are preprocesses to enhance the regions containing license plates and reduce noise. This preprocessing step may include operations such as resizing, grayscale conversion, noise reduction, and contrast enhancement. Next, object detection algorithms such as Haar cascades or deep learning-based techniques like YOLO (You Only Look Once) are utilized to detect candidate regions in the image/frame that potentially contain license plates. After candidate regions are identified, a series of image processing techniques such as morphological operations, contour detection, and character segmentation are employed to isolate and extract the license plate characters. Machine learning models like Support Vector Machines (SVM), Convolutional Neural Networks (CNNs) are then applied to recognize the characters on the license plate.