Vehicle License Research Articles

Exploring the Impact of Parkinson's Disease on Driving: A Population-Based Survey.

Persons with Parkinson's disease (PD) experience progressive motor and non-motor symptoms which may influence their ability to drive a car. This is experienced as a massive challenge by many affected individuals, for whom being able to drive a car is vital to maintain functional independence. We assessed how the diagnosis of PD affected the possession of a driving license, how people with PD had adapted their driving style, and to what extent they had communicated about their driving ability with their healthcare professionals. We also evaluated their knowledge on insurance- and Driver and Vehicle Licensing Agency (DVLA)-related implications. A cross-sectional 10-item survey was completed by 540 participants of a population-based cohort of persons with PD in the Netherlands (PRIME-NL study). Participants had a mean age of 70 years and disease duration of 7.3 years. 84% possessed a valid driving license. Of those who gave up their license, this was done mostly (78%) on a voluntarily basis. Forty percent of those with a driving license adjusted their driving style. Over 50% of respondents had not discussed the impact of PD on their driving ability with their healthcare professionals. Although not compulsory by Dutch law, 52% of the respondents had informed the DVLA about their diagnosis. This study highlights the need for information and support from healthcare professionals to proactively address driving in their clinical practice. This will help persons with PD in their efforts to maintain their driving license for as long as possible.

A Hybrid Method Based on Haris Corner and Maximally Stable Extremal Regions for Vehicle Plate Number Detection

An intelligent transportation system (ITS) is a crucial element of a smart city, and it includes the capability to identify vehicle license plates. Utilizing digital image processing is a cost-effective method for identification. The tiny size of the number plate is just one of the many unfortunate difficulties with this approach. Hence, this research is crucial, particularly in enhancing the precision of detection. The Harris Corner approach is one way to locate the motor vehicle number plate location. However, the Harris corner method could be more optimal for analyzing moving vehicle videos as input. Since frame-by-frame variations in the video input's lighting, accuracy cannot be maximized. Furthermore, the vehicle and license plate appear significantly smaller due to the camera's distant positioning. Consequently, the authors suggest a hybrid approach using the Maximally Stable Extremal Regions (MSER) method. The Harris Corner and MSER methods will concurrently identify the initial position of the vehicle number plate. Moreover, the initial detection outcomes of the two techniques are compared and adjusted to achieve a more precise determination of the placement of the number plate. The results show that integrating the MSER method into the Harris Corner method yields a hybrid approach that enhances accuracy by 13%. Furthermore, it visually represents the selected number plate with greater accuracy.

Application of OCR Technology for Vehicle License Plate Detection and Yolo V8 for Car Counting

The development of urban mobility and the growth in the number of motorized vehicles have created significant security challenges in managing parking areas. In this context, the use of Automatic License Plate Recognition (ALPR) technology and object detection emerges as a potential solution. Through the implementation of a smart camera system equipped with number plate detection and object detection, this research aims to reduce the risk of human error in recording parking data and prevent illegal parking. In answering this problem, this research offers a solution in the form of developing a smart camera system that is able to detect the presence of vehicles and recognize number plates with a high level of accuracy. Through the integration of ALPR and object detection, this system is expected to be able to overcome obstacles that arise in parking management, increase efficiency, and effectively prevent illegal parking. The implementation of automatic barriers, access cards and integrated CCTV monitoring will further strengthen the security of the parking area. With the research results, the proposed smart camera system succeeded in achieving vehicle and license plate detection accuracy levels above 90%. The quantitative and qualitative data collected supports the effectiveness of this solution in improving parking management and parking area security. In conclusion, this research makes a positive contribution in facing security challenges in cities through the use of advanced technology, opening up the potential for widespread application in the context of continuously developing urban mobility.

Recognition of vehicle license plates in highway scenes with deep fusion network and connectionist temporal classification

AbstractLicense plate recognition is crucial in Intelligent Transportation Systems (ITS) for vehicle management, traffic monitoring, and security inspection. In highway scenarios, this task faces challenges such as diversity, blurriness, occlusion, and illumination variation of license plates. This article explores Recurrent Neural Networks based on Connectionist Temporal Classification (RNN‐CTC) for license plate recognition in challenging highway conditions. Four neural network models: ResNet50, ResNeXt, InceptionV3, and SENet, all combined with RNN‐CTC are comparatively evaluated. Furthermore, a novel architecture named ResNet50 Deep Fusion Network using Connectionist Temporal Classification (ResNet50‐DFN‐CTC) is proposed. Comparative and ablation experiments are conducted using the Highway License Plate Dataset of Southeast University (HLPD‐SU). Results demonstrate the superior performance of ResNet50‐DFN‐CTC in challenging highway conditions, achieving 93.158% accuracy with a processing time of 7.91 ms, outperforming other tested models. This research contributes to advancing license plate recognition technology for real‐world highway applications under adverse conditions.

Spatial correction and Deblurring fusion algorithm for vehicle license plate images based on deep learning

Spatial correction and Deblurring fusion algorithm for vehicle license plate images based on deep learning

Application of Canny Method to Detect Vehicle License Plate in Tanjung Balai City Government Mess Area

Vehicles have a license plate that serves to be the identity of a vehicle. The shape of the plate is in the form of a piece of metal mounted on the vehicle as an official identity. Making a license plate or Motor Vehicle Number Sign in Indonesia is regulated in Government Regulation No. 60 of 2016 with a validity period of 5 years. The regulation is about the type and tariff of Non-Tax State Revenue (BNBP), and has been officially enacted on January 6, 2017, by replacing Government Regulation No.50 of 2010, quoted from the Kompas newspaper website. Image is one of the components of multimedia that plays an important role because it contains information in visual form. Images have more information that can be conveyed than in the form of text. An image is a collection of image elements (pixels) that as a whole record a scene through a visual sensory (camera). Canny edge detection can detect edges with a minimum error rate, canny edge detection has a difference with other operators because it uses a Gaussian Derivative Kernel that can refine the appearance of the image. Good location can minimize the distance of edge detection produced by processing, so that the location of the edge can be detected similar to the real edge. The accuracy value of applying this method reaches 99.88%-100%. And lastly, one response to single edge that can produce a single edge, not giving false edges.

Hetero-associative Memory Based New Iraqi License Plate Recognition

نتيجة للتطورات الأخيرة في أبحاث الطرق السريعة بالإضافة إلى زيادة استخدام المركبات، كان هناك اهتمام كبير بنظام النقل الذكي الأكثر حداثة وفعالية ودقة (ITS) في مجال رؤية الكمبيوتر أو معالجة الصور الرقمية، يلعب تحديد كائنات معينة في صورة دورًا مهمًا في إنشاء صورة شاملة. هناك تحدٍ مرتبط بالتعرف على لوحة ترخيص السيارة (VLPR) بسبب الاختلاف في وجهة النظر، والتنسيقات المتعددة، وظروف الإضاءة غير الموحدة في وقت الحصول على الصورة والشكل واللون، بالإضافة إلى الصعوبات مثل ضعف دقة الصورة ، الصورة الباهتة ، الإضاءة السيئة، التباين المنخفض، يجب التغلب عليها. اقترحت هذه الورقة نموذجًا باستخدام تعديل الذاكرة الترابطية ثنائية الاتجاه (MBAM)، وهي نوع واحد من الذاكرة الترابطية غير المتجانسة، وتعمل MBAM على مرحلتين)مرحلتي التعلم والتقارب) للتعرف على اللوحة، ويمكن لهذا النموذج المقترح التغلب على تلك الصعوبات بسبب قدرة الذاكرة الترابطية لـ MBAM على قبول الضوضاء وتمييز الصور المشوهة، وكذلك سرعة عملية الحساب نظرًا لصغر حجم الشبكة. نتيجة دقة تحديد منطقة اللوحة هي 99.6٪، ودقة تجزئة الأحرف 98٪، والدقة المحققة للتعرف على الأحرف هي100 ٪ في ظروف مختلفة.

Vehicle License Plate Detection and Recognition using OpenCV and Tesseract OCR

License plate recognition (LPR) is essential as the number of vehicles increases and the human ability to accomplish this task is limited. If human labor is used to manage these, it will take a lot of time and energy and cause a discrepancy. License Plate Recognition (LPR) is an advanced technology that leverages optical character recognition (OCR) and various image processing methods to read vehicle license plates automatically. Typically, an LPR system comprises two primary components: detecting vehicles and their license plates and recognizing the alphanumeric characters displayed on those plates. This study explores the use of OpenCV for license plate detection and Tesseract OCR for character recognition. In this research, the dataset for training and testing the system included 100 license plates evenly split between plates featuring English and Lao characters. The Lao license plates presented unique complexities due to their specific characteristics. The experimental setup involved processing images of license plates taken from multiple angles. The system's performance was evaluated based on the speed and accuracy of line and character recognition. For English character plates, the recognition process took 0.12 seconds with an accuracy of 98.8%. In contrast, the Lao character plates required 0.24 seconds, achieving an accuracy rate of 89.42%.

Discrepancy between medical conditions self-reported by bus drivers and medical records.

Medical incapacity at the wheel is a rare but high-profile factor in accident causation. The UK Driver and Vehicle Licensing Agency (DVLA) does not require a review of medical records on the application for a bus licence, but applicants sign a self-declaration of medical history. There is debate over what, if any, verification of medical information is required for doctors who complete the medical assessment. To assess how self-declaration compares against General Practitioner (GP) patient records for a series of bus drivers undergoing driver licensing assessment in a 12-month period. Review of GP reports received for bus driver medicals undertaken in a 12-month period between 1 July 2022 and 30 June 2023. Of the 145 medicals undertaken, 12 (8.3%) GP reports contained undeclared medical conditions that required further evaluation and may have impacted on fitness to drive. Studies into the contribution of medical incapacity at the wheel to vehicle accidents are sparse, as, thankfully, are the events themselves. Nevertheless, given the updated General Medical Council guidance to doctors on confidentiality and public protection responsibilities, and evidence to suggest that doctors' knowledge of the DVLA guidelines is poor, it might be pertinent for the DVLA to reconsider its approach to driver self-reporting in some circumstances, given the discrepancy highlighted here.

AKT question relating to the Driving Vehicle Licensing Agency (DVLA) criteria

AKT question relating to the Driving Vehicle Licensing Agency (DVLA) criteria

AKT answer relating to the Driving Vehicle Licensing Agency (DVLA) criteria

AKT answer relating to the Driving Vehicle Licensing Agency (DVLA) criteria

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.

Integrated Notification System for Smart Parking Security Using Bot Telegram

Notification system for smart parking system is very important for parking security. The purpose of this research is to create a system used in parking security in the technical faculty of mataram university. The method used in this research is the flow of making a notification system using a telegram bot, namely determining the notification using a telegram bot and then creating a telegram bot that will send notifications to security guards, after the bot has been created, then coding the notification system so that the notification can run according to existing rules. The results showed that the notification system made runs according to predetermined rules as seen from the success of sending notifications to the security guard group. The conclusion is that the use of this notification system is a solution to parking security problems. This notification system can provide accurate information to security officers regarding information on vehicle data mismatches and vehicle license plates so that vehicles in the parking area can be monitored.

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.

A Real-Time License Plate Detection and Recognition Model in Unconstrained Scenarios.

Accurate and fast recognition of vehicle license plates from natural scene images is a crucial and challenging task. Existing methods can recognize license plates in simple scenarios, but their performance degrades significantly in complex environments. A novel license plate detection and recognition model YOLOv5-PDLPR is proposed, which employs YOLOv5 target detection algorithm in the license plate detection part and uses the PDLPR algorithm proposed in this paper in the license plate recognition part. The PDLPR algorithm is mainly designed as follows: (1) A Multi-Head Attention mechanism is used to accurately recognize individual characters. (2) A global feature extractor network is designed to improve the completeness of the network for feature extraction. (3) The latest parallel decoder architecture is adopted to improve the inference efficiency. The experimental results show that the proposed algorithm has better accuracy and speed than the comparison algorithms, can achieve real-time recognition, and has high efficiency and robustness in complex scenes.

Docker Container-Based Framework of Apache Kafka Node Ecosystem: Vehicle Tracking System by License Plate Recognition on Surveillance Camera Feeds

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.

Two-stage HOG/SVM for license plate detection and recognition

Automatic license plate recognition (ALPR) is one of the technologies used in intelligent transport systems (ITS) to read vehicle license plates automatically. The extracted information has various potential applications, including but not limited to an electronic payment gateway, a system for paying parking fees, road surveillance, and managing traffic flow. In this paper, we propose an efficient method to detect and identify the Algerian license plate (LP). This method consists of a two-stage algorithm that combines the histogram of oriented gradients (HOG) with the support vector machine (SVM) classifier. The purpose of the first stage of HOG/SVM is the detection of the LP, while the recognition of the digits is accomplished by the second stage of HOG/SVM. As first contribution, a dataset of standard Algerian LP not available elsewhere is built (DZLP dataset), The second is a proposal of a very efficient pre-processing step for LP detection and digit recognition. Experimental results show that the proposed approach yields very high license plate and average digits recognition rates, which of 97.5% and 99.46%, respectively.

Beyond the Marketing Hype: Analysis of Communication Campaigns of Electric Vehicles Eco-Friendly Image and Battery Waste Concerns

Electric Vehicles (EVs) are gaining widespread attention for their environmentally friendly nature and potential to reduce air pollution. As non-renewable fossil fuels deplete, the need for sustainable alternatives like EVs becomes imperative. The EV market emphasizes the cost-effectiveness of electric fuel and extended travel range, despite the initial higher cost of purchasing an EV. In Indonesia, the government encourages EV adoption through subsidies and improved electric refuelling infrastructure, including exempting EV users from the odd-even vehicle license plate rule in Jakarta.
 To promote EV adoption, brands employ diverse marketing strategies, urging customers to embrace EVs as agents of environmental change. However, the lack of transparency in how EV brands handle nickel-based battery waste, with a limited lifespan, raises environmental concerns. The accumulation of unusable batteries could pose soil pollution risks as EV usage grows. This research aims to explore the environmental implications of battery waste from EVs in Indonesia and analyze how EV brands address these concerns in their marketing communication campaigns.
 Adopting a cybernetic approach, the study examines the interconnectedness of marketing communication and consumer buying decisions. Qualitative research is utilized, drawing on various EV studies, facts about EV brand communication, and interviews with experts to provide a comprehensive analysis. Although battery waste might not be a pressing issue in Indonesia currently, inadequate waste handling raises potential future concerns about soil and water contamination. Addressing battery waste responsibly is crucial for promoting sustainable EV adoption.

A Metaphor Analysis on Vehicle License Plate Detection using Yolo-NAS and Yolov8

There have been significant advancements in object detection in recent years, notably with the emergence of the YOLO approach. In this paper, we proposed a thorough comparison between YOLO-NAS and YOLOv8 specifically for detecting vehicle license plates based on the analysis of experimental results. Real-time vehicle license plate detection can have a wide range of applications like crime prevention, surveillance, traffic-pattern analysis, automated toll collection, etc. Our evaluation uses a diverse dataset capturing vehicle images from various angles and levels of occlusion to ensure a comprehensive assessment of the models' capabilities. The metrics considered include precision, recall, and mAP, offering insights into detection accuracy and false positive rates. The results reveal nuanced performance differences between YOLO-NAS and YOLOv8 in vehicle license plate detection tasks. YOLO-NAS demonstrates superior accuracy in specific scenarios due to its optimized architecture. Conversely, YOLOv8 shows notable efficiency gains, especially regarding inference speed, making it a compelling choice for real-time applications. Here, we contribute valuable insights to the ongoing discussion on object detection methodologies, assisting practitioners in making informed decisions when selecting a model for vehicle license plate detection applications. The findings underscore the importance of considering trade-offs between accuracy and computational efficiency in specific use cases

A Robust Approach for Licence Plate Detection Using Deep Learning

Intelligent transport systems must be developed due to the rising use of vehicles, particularly cars. In the field of computer vision, the identification of a vehicle's licence plate (LP) has been crucial. Various methods and algorithms have been used for the detection process. It becomes challenging to find similar photos, nevertheless, because the features of these plates change depending on colour, font, and language of characters. The research proposes a powerful deep learning framework based on feature extraction using convolutional neural networks and localization using canny-edge detection. Three steps make up the model's operation. An improved approach integrating the usage of bilateral filters and Canny edge detection is used for the processes of segmentation and localization. Further, a CNN architecture is used to extract features from images and classify the presence of licence plates in unseen vehicles. If present, the stage is followed by recognition of numbers written on the plates. An extensive experimental investigation takes place using three datasets namely Stanford Cars, Car Licence Plate Detection dataset and Indian Licence Plates Database. The attained simulation outcome ensures a superior performance over existing techniques in a significant way.