License Plate Recognition Research Articles

A Comparative Study on Detection and Recognition of Nonuniform License Plates

This paper presents a comparative study on license plate detection and recognition algorithms in unconstrained environments, which include varying illuminations, nonstandard plate templates, and different English language fonts. A prime objective of this study is to assess how well these models handle such challenges. These problems are common in developing countries like Pakistan where diverse license plates, styles, and abrupt changes in illuminations make license plates detection and recognition a challenging task. To analyze the license plate detection problem Faster-RCNN and end-to-end (E2E) methods are implemented. For the license plate recognition task, deep neural network and the CA-CenterNet-based methods are compared. Detailed simulations were performed on authors’ own collected dataset of Pakistani license plates, which contains substantially different multi-styled license plates. Our study concludes that, for the task of license plate detection, Faster-RCNN yields a detection accuracy of 98.35%, while the E2E method delivers 98.48% accuracy. Both detection algorithms yielded a mean detection accuracy of 98.41%. For license plate recognition task, the DNN-based method yielded a recognition accuracy of 98.90%, while the CA-CenterNet-based method delivered a high accuracy of 98.96%. In addition, a detailed computational complexity comparison on various image resolutions revealed that E2E and the CA-CenterNet are more efficient than their counterparts during detection and recognition tasks, respectively.

New ways of collecting urban freight traffic data and applications for urban freight policies and research

The current quality of data on urban logistics traffic patterns is poor. Data are scarce, heterogeneous, often old and expensive to collect. This research looks at new ways to collect urban freight data and make that data available to both policy-makers and academia. Three case studies are examined: Rotterdam, the Netherlands; Brussels, Belgium; and Barcelona, Spain. They help identify new sources of data from traffic and parking policies: automated number plate recognition cameras, an on-street delivery smartphone app, and a truck pricing scheme. Both the context in which new data sources of interest to urban logistics are produced, and the level of appropriation of these data by local stakeholders and the research community are examined. We then construct an analysis grid to evaluate the diversity of systems (technical and socio-technical) producing data relevant to urban logistics. This analysis framework can be used to facilitate comparisons between data sources and thus help future case studies. In the three cases, we found that local governments use the data more than researchers do. The issue of data dissemination raises questions about costs (who is to bear the cost of storing, maintaining and extracting data sets?) as well as regulatory questions. We conclude that local administrations should be encouraged to work towards harmonizing data collection methods and data sharing conditions in order to improve the quality and comparability of the data. Cities in countries with little access to new sources of data, such as France for regulatory reasons, should try to find ways to create data source opportunities. In all countries, urban freight research communities should be encouraged to use the now increasingly available data.

A Review of Expert Systems Integration in Signal Plan Optimisation

In urban networks, periodic peak traffic congestion often occurs during the day, namely in the morning and afternoon hours. Due to spatial constraints and the inability to increase capacity through physical road expansion, modern traffic management increasingly relies on Intelligent Transport Systems (ITS) solutions. One such solution is the integration of automatic licence plate recognition, an expert system and microsimulation tools aimed at optimising the network performance of signalised intersections within a network. Based on real-time and historical data on individual vehicle trajectories, the system predicts the route of each vehicle through the observed segment of the traffic network, determines the network load and proposes optimal signal plans. This paper provides an overview of conducted research related to the optimisation of signal plans utilising expert systems. Mathematical models for capacity and load determination, as well as computational intelligence-based systems used for signalised intersection management strategies, are described. Finally, the paper proposes a basic framework and guidelines related to the suggested system, highlighting open questions and potential challenges in its development.

Predictive pixel-wise optical encoding: towards single-shot high dynamic range moving object recognition

Conventional low dynamic range (LDR) imaging devices fail to preserve much information for further vision tasks because of the saturation effect. Thus, high dynamic range (HDR) imaging is an important imaging technology in extreme illuminance conditions, which enables a wide range of applications, including photography, autonomous driving, and robotics. Mainstream approaches require multi-shot methods because the conventional camera can only control the exposure globally. Although they perform well on static HDR imaging, they face a challenge with real-time HDR imaging for motional scenes because of the artifact and time latency caused by multi-shot and post-processing. To this end, we propose a framework, termed POE-VP, which achieves single-shot HDR imaging via a pixel-wise optical encoder driven by video prediction. We use highlighted motional license plate recognition as a downstream vision task to demonstrate the performance of POE-VP. From the results of simulation and real scene experiments, we validate that POE-VP outperforms conventional LDR cameras by more than 5 times in recognition accuracy and by more than 200% in information entropy. The dynamic range could reach 120 dB, and the captured data size is verified to be lower than mainstream multi-shot methods by 67%. The running time of POE-VP is also validated to satisfy the needs of high-speed HDR imaging.

Research on the detection and recognition system of target vehicles based on fusion algorithm

<p>In modern society, the monitoring of vehicles is paramount for upholding traffic safety and order. This paper delves into a Graphical User Interface (GUI)-driven system for vehicle target detection and integration. This system seamlessly integrates three pivotal functionalities: vehicle type recognition, license plate recognition, and driver face recognition. It automates the vehicle inspection assessment process through an intuitive user interface. Specifically, for license plate recognition, the system leverages traditional computer vision techniques, including color and grayscale processing, radon transform, morphological operations, edge detection, character segmentation, and character recognition. The driver face recognition module incorporates methods such as image gray scaling, Gaussian filtering for denoising, face detection, and feature extraction. Meanwhile, vehicle type recognition is achieved by extracting Histogram of Oriented Gradients (HOG) features and utilizing a Support Vector Machine (SVM) classifier. Experimental findings highlight the system’s remarkable recognition accuracy, offering a highly efficient and dependable solution for the automated inspections of vehicles.</p>

Is It Just Dumb Luck? The Challenge of Getting Access to Public Records Related to Smart City Technology

Many state and local governments have begun to rely heavily on Smart City technologies, including license plate readers, gun-shot detection equipment, wastewater monitoring tools, and more. But the public often has little oversight with regard to their purchase, installation, and use. We analyze public records laws and relevant case law to examine whether the laws provide access to records about, and produced by, Smart City technologies. Even in states that broadly define “public record,” officials regularly rely on exemptions—often citing law enforcement or privacy concerns—to deny access. Further, many Smart City technologies have not been addressed by the legislatures and courts, but legislation and litigation involving two technologies (police body-worn cameras and police dash cameras) have advanced—offering some optimism.

The Achilles’ Heel of License Plate Recognition Parking Enforcement: Balancing Privacy Protection and Enforcement

The Achilles’ Heel of License Plate Recognition Parking Enforcement: Balancing Privacy Protection and Enforcement

Robust License Plate Recognition in OCC-Based Vehicle Networks Using Image Reconstruction

With the help of traffic lights and street cameras, optical camera communication (OCC) can be adopted in Internet of Vehicles (IoV) applications to realize communication between vehicles and roadside units. However, the encoded light emitted by these OCC transmitters (LED infrastructures on the roadside and/or LED-based headlamps embedded in cars) will generate stripe patterns in image frames captured by existing license-plate recognition systems, which seriously degrades the accuracy of the recognition. To this end, we propose and experimentally demonstrate a method that can reduce the interference of OCC stripes in the image frames captured by the license-plate recognition system. We introduce an innovative pipeline with an end-to-end image reconstruction module. This module learns the distribution of images without OCC stripes and provides high-quality license-plate images for recognition in OCC conditions. In order to solve the problem of insufficient data, we model the OCC strips as multiplicative noise and propose a method to synthesize a pairwise dataset under OCC using the existing license-plate dataset. Moreover, we also build a prototype to simulate real scenes of the OCC-based vehicle networks and collect data in such scenes. Overall, the proposed method can achieve a recognition performance of 81.58% and 79.35% on the synthesized dataset and that captured from real scenes, respectively, which is improved by about 31.18% and 24.26%, respectively, compared with the conventional method.

Evaluative Performance Program E-Tilang di Simpang Raya Kota Surabaya

ETLE (Electronic Traffic Law Enforcement) is a set of electronic devices that use Automatic License Plate Recognition (ANPR) technology in the form of cameras that automatically detect motorized vehicles and cars with traffic violations E-TLE system, which is part of law enforcement efforts, this system aims to reduce problems caused by traffic violations, such as congestion and accidents, and ensure order on the highway. The e-Tilang system was pioneered by the East Java Regional Police, especially the Surabaya Police, which has this innovation as a crucial role in improving safety and regularity of electronically based traffic, the implementation of ETLE tickets in Surabaya City has increased in 2022 and 2023 compared to 2021. Which in 2021 the ETLE program was intensified due to the Covid-19 pandemic in order to increase the efficiency of technology-based services. The ETLE camera system uses CCTV cameras that are monitored directly by Police Officers with a total of 22 monitoring camera points in several parts of Surabaya City. where each officer oversees 4 areas with 8 CCTVs on the monitoring screen. Although it has had a positive impact on the implementation of ticketing, there are many things that should be evaluated from the process of running E-TLE at Surabaya Police Station. Mainly in the process of implementing and providing maximum fines, as well as the limited number of employees and time in ETLE supervision. So that the Process on Performance and the Facilities used should be evaluated in the running of the ETLE system in Surabaya City. The method used in this research is descriptive qualitative. The focus of this research is to find out the results of the Evaluation of the E-Ticket Policy Program (E-TLE) at the Surabaya City Road Intersection The theory used in the research is Gupta & Jana's Evaluative performance theory. There are 4 indicators from this theory, namely total costs and fines, quality of planning and control, quality of decision making and system characteristics. The data collection technique in this study is by conducting interviews, observation and documentation. In this research, from these 4 indicators, some are quite appropriate, but there are still many shortcomings that should be evaluated from the system and the quality of performance of the Surabaya police litas unit. The performance evaluation of the E-Tilang program in Surabaya shows success in managing total costs and fines, the quality of planning and control, and the quality of decision making. This program includes the use of 40 CCTV camera units and fiber optic networks that support the effectiveness of law enforcement, as well as the management of fair fines. Careful planning and better coordination after the evaluation ensured efficiency in recording violations using gadgets and the E-Tilang application.

Efficient license plate recognition in unconstrained scenarios

Automatic license plate recognition (ALPR) is a critical technology for intelligent transportation systems. Most existing ALPR methods are focused on specific application scenarios. Although there are a few methods that focus on unconstrained scenarios, they are very time-consuming. In this work, we propose an efficient ALPR (EALPR) framework, where we can handle distorted license plates (LP) caused by perspective problems with high efficiency. We design a light LPD structure based on efficient object detection methods and use anchor-free strategies for LPD to alleviate the problem of expensive costs. Benefitting from these optimizations and a united framework structure, the proposed EALPR has real-time efficiency. We evaluate our method on five datasets and the results show that our method achieves state-of-the-art accuracy: 98.15% on OpenALPR(EU), 95.61% on OpenALPR(BR), 99.51% on AOLP(RP), 88.81% on SSIG, 79.41% on CD-HARD. Additionally, our method achieves an impressive speed of 74.9 FPS (Frames Per Second), outperforming existing approaches and demonstrating its efficiency. Our source code can be accessed at https://github.com/wechao18/Efficient-alpr-unconstrained.

Research and Design of Intelligent Parking Management System based on the YOLO Algorithm

Given the difficulty in managing parking lots and inefficient utilization of parking spaces at university and college campuses, this paper designs an intelligent campus parking management system with functions such as license plate recognition (LPR), online parking space reservations, campus parking navigation, and mobile app payment through the parking space detection technology based on You Only Look Once (YOLO) algorithm, Internet of Things (IoT) technology, and cloud platform technology. This system obtains information relating to license plates, parking spaces, etc. from sensing nodes with the IoT technology and transmits the collected information through NB-IoT technology to a cloud platform for storage and management to facilitate information exchange between hardware and software. This paper describes the architecture of the intelligent parking management system, parking space detection technology based on the YOLOv4 algorithm, and hardware and software design of the system. The paper proposes an effective solution to parking problems facing colleges and universities. The paper concludes that the intelligent campus parking management system based on the YOLOv4 algorithm can prevent the haphazard parking of vehicles and traffic congestion at open campuses and therefore has important value for application and popularization.

Chinese License Plate Recognition Based on OpenCV and LPCR Net

Chinese License Plate Recognition Based on OpenCV and LPCR Net

Enhanced Malaysian License Plate Recognition System Using an Improved YOLOv2 Model

License Plate Recognition (LPR) has gained popularity among researchers due to its wide range of applications, including law enforcement, monitoring, and toll gate systems. However, existing LPR systems still require improvements to achieve optimum accuracy and speed. The advancements in Convolutional Neural Network (CNN) variants offer potential solutions for these challenges. This primary aim of this system is to ensure accurate and efficient recognition of the vehicle plate characters using CNN techniques. This research utilizes two CNN network architectures for deep object detection to address the Malaysian License Plate Recognition (MLPR) task. The first network is designed to detect the license plate, while the second is responsible for recognizing the characters on the plate. Both networks are cascaded from the architecture of two-stage YOLOv2, providing promising speed and accuracy. The MLPR system achieved an accuracy of 98.75% and a processing speed of 0.0104 seconds, using a total of 2,200 license plate images. In conclusion, the system adapted from deep object detection techniques presents a promising solution for the MLPR problem, based on the achieved accuracy and speed.

Comparative Analysis of Deep Learning Algorithms in Fire Detection

As technology advances rapidly, deep learning applications, a subset of machine learning, are becoming increasingly relevant in various aspects of our lives. Essential daily applications like license plate recognition and optical character recognition are now commonplace. Alongside current technological progress, the development of future-integrated technologies such as suspicious situation detection from security cameras and autonomous vehicles is also accelerating. The success and accuracy of these technologies have reached impressive levels. This study focuses on the early and accurate detection of forest fires before they cause severe damage. Using primarily forest fire images from datasets obtained from Kaggle, various deep learning algorithms were trained via transfer learning using MATLAB. This approach allowed for comparing different deep learning algorithms based on their efficiency and accuracy in detecting forest fires. High success rates, generally exceeding 90%, were achieved.

Vehicle And Driver Recognition for Access Control

The following paper proposes an advanced vehicle and driver recognition system to enhance security at vehicular entry-exit points in response to the growing needs of more efficient and secure access control systems at these checkpoints. The proposed system will integrate license plate recognition and facial recognition technologies using state of-the-art machine-learning models from Ultralytics: YOLOv8 and EasyOCR, respectively. It works in three stages: license plate detection, character alpha-numeric identification, and then driver identity with facial recognition. Hence, the two-tier authentication process based on license plate detection and facial identification ensures further prevention from unauthorized entry. Preliminary evaluation of the system also produced very high values for precision, recall, and the mean average precision, indicating that the VDR system offers substantial advances in effectiveness and security when compared to conventional methodologies for access control.

Enhanced license plate recognition using deep learning and block-based approach

This study investigates the effectiveness of current deep learning techniques in license plate detection and makes essential contributions. Instead of classifying the characters on Turkish license plates with a single classifier, the characters are divided into blocks of numbers and letters using various image processing techniques, and a separate classifier is used for each block. The proposed approach improves character classification accuracy and license plate recognition accuracy. This approach eliminated the possibility of misclassifying similar letters and numbers and improved the character classification accuracy from 95.9% to 99.6%. In addition, a new character feature dataset was created, and a deep learning model was trained on this dataset. Integrating this model into the system increased the classification accuracy to 99.7%. The YOLOv8 object detection model, trained using CUDA technology, achieved a mAP of 98.9%. The overall accuracy of the whole system in license plate recognition reached 97.3%. This study proves the effectiveness of current deep learning methods and the proposed block-based character recognition approach in license plate recognition.

XAI-SALPAD: Explainable deep learning techniques for Saudi Arabia license plate automatic detection

In recent decades, automatic license plate recognition (ALPR) has emerged as a critical application of artificial intelligence in intelligent transportation systems (ITS), addressing a multitude of existing issues. Several algorithms have been developed to improve ALPR accuracy under a variety of conditions, each with its own set of strengths and limitations. The KSA intends to digitize traditional services as part of its Vision 2030 initiative. In this methodology, the ALPR system for accurately recognizing KSA's license plates (LP) leverages deep neural networks (DNNs) as a powerful technique, capable of performing effectively in unconstrained environments with images taken under various conditions, even when the training data is limited. The approach employs YOLOv8 to detect LPs and alphanumeric characters in real-time, followed by using a convolutional neural network (CNN) for character recognition. According to our finding, the YOLOv8 model outperforms other models in the identification of LPs, achieving remarkable accuracy and F1 scores (mAP@0.5 = 0.96 and mAP@0.95 = 0.97). As a result, YOLOv8 was chosen for SLP character recognition. This strategy is appropriate for ITS since it tackles a number of issues, like decreasing car theft and improving public safety. Furthermore, a locally interpretable agnostic model (LIME) offers justifications that help users better understand the model. The system's provision of clear explanations for its decisions not only satisfies performance standards but also corresponds with the increasing need for explainable AI, guaranteeing an efficient and responsible implementation.

Large-Area Perovskite Nanocrystal Metasurfaces for Direction-Tunable Lasing.

Perovskite nanocrystals (PNCs) are attractive emissive materials for developing compact lasers. However, manipulation of PNC laser directionality has been difficult, which limits their usage in photonic devices that require on-demand tunability. Here we demonstrate PNC metasurface lasers with engineered emission angles. We fabricated millimeter-scale CsPbBr3 PNC metasurfaces using an all-solution-processing technique based on soft nanoimprinting lithography. By designing band-edge photonic modes at the high-symmetry X point of the reciprocal lattice, we achieved four linearly polarized lasing beams along a polar angle of ∼30° under optical pumping. The device architecture further allows tuning of the lasing emission angles to 0° and ∼50°, respectively, by adjusting the PNC thickness to shift other high-symmetry points (Γ and M) to the PNC emission wavelength range. Our laser design strategies offer prospects for applications in directional optical antennas and detectors, 3D laser projection displays, and multichannel visible light communication.

License Plate Recognition Using Three-Dimensional Rotated Character Recognition and Instance Segmentation by Deep Learning

License plate recognition is currently used in various situations, such as parking lot vehicle management and the tracking of wanted vehicles. These applications require recognition from all possible camera angles. Therefore, we propose a method for license plate recognition in images captured from various camera angles. First, the license plate area is detected from the input image using the YOLOv5 object detection method. The subsequent process can be performed in two manners: by rotating the license plate to the front using projective transformation and trapezoidal correction, or by using graph matching. The first method performs Hough transforms or linear approximations as preprocessing, and then calculates the rectangle surrounding the license plate from the YOLOv5 output. After performing trapezoidal correction, the maximally stable extremal regions (MSER) are used to detect character candidates, and characters are recognized using 3D rotated character recognition. The second method performs character candidate detection and recognition without trapezoidal correction, followed by graph matching. These methods are versatile because they do not depend on the layout of license plates, which varies among countries and regions. Two types of datasets were used: a set of images containing Japanese license plates collected by ourselves, and a set of publicly available images containing Taiwanese license plates. Two recognition rates were evaluated: the license plate character recognition rate and license plate recognition rate in which all characters in one plate are recognized. The license plate character recognition rates using graph matching were 91.9% for the Japanese license plates and 89.3% for the Taiwanese license plates. The license plate recognition rates were 84.0% and 84.5%, respectively.

Road network traffic flow prediction: A personalized federated learning method based on client reputation

Accurate traffic flow prediction can provide effective decision-making support for traffic management, alleviate traffic congestion, and improve road traffic efficiency. Traffic flow data contains personal privacy information, such as vehicle trajectories, driving speed, etc. However, most existing research focuses on using all local data to jointly construct prediction models, facing data security and privacy issues. In response to these challenges, this paper presents a Personalized Federated Learning method based on Client Reputation for traffic flow prediction. This method avoids direct sharing of original data by performing model training and information aggregation locally, thereby better protecting user privacy. We initially constructed the foundational model by integrating Graph Neural Networks (GCNs) and Gated Recurrent Units (GRUs) for federated learning. GCNs are capable of capturing the spatial relationships between nodes in a road network, thereby extracting the spatial features of traffic flow. On the other hand, GRUs has advantages at handling sequential data, effectively capturing the dynamic temporal characteristics of traffic flow. Consequently, by combining GCNs and GRUs, we are able to build a model that captures both the spatial and temporal features of traffic flow. Subsequently, we employed a federated learning framework to train the model. In the training process, we propose two evaluation algorithms to evaluate the reputation of all clients participating in federated learning. Then, different weights are customized for each client through a personalized algorithm based on the client's reputation to improve its personalization. Finally, we validated the method on the License Plate Recognition (LPR) datasets collected in Changsha city, China. The experimental results indicate that the method can achieve good prediction performance and stability while protecting the privacy of all participants.