Vehicle License Plate Research Articles

Enhancing automatic license plate recognition in Indian scenarios

Automatic license plate recognition (ALPR) technology has gained widespread use in many countries, including India. With the explosion in the number of vehicles plying over the roads in the past few years, automating the process of documenting vehicle license plates for use by law enforcement agencies and traffic management authorities has great significance. There have been various advancements in the object detection, object tracking, and optical character recognition domain but integrated pipelines for ALPR in Indian scenarios are a rare occurrence. This paper proposes an architecture that can track vehicles across multiple frames, detect number plates and perform optical character recognition (OCR) on them. A dataset consisting of Indian vehicles for the detection of oblique license plates is collected and a framework to increase the accuracy of OCR using the data across multiple frames is proposed. The proposed system can record license plate readings of vehicles averaging 527.99 and 2157.09 ms per frame using graphics processing unit (GPU) and central processing unit (CPU) respectively.

Vehicle wheel load positioning method based on multiple projective planes

AbstractComputer vision‐based vehicle load monitoring methods could obtain spatiotemporal data of vehicle loads, which is important for bridge monitoring and operation. However, during the process of vehicle detection and tracking, current research usually focuses on the vehicle as a whole, and there is a lack of research on the accurate positioning of vehicle wheel loads. For the fatigue analysis of orthotropic steel deck, stress at the structural details belongs to the typical third‐class system, and related research requires accurate wheel load position. Based on the principle of camera imaging, this study proposes an innovative vehicle wheel load location method based on vehicle license plate detection and multiple projective planes, and the accurate positioning of the vehicle center is achieved by the projective relationship matrix of different planes. Then, accurate measurement of the lateral wheelbase is achieved through secondary detection and projective transformation. Further, accurate wheel load tracking for fatigue research is achieved by the multi‐objective tracking algorithm. Based on theoretical analysis and practical application results, the effectiveness and accuracy of this method have been verified. Different from traditional positioning methods based on vehicle detection boxes and 3D reconstruction boxes, the proposed method has higher accuracy and will play a fundamental role in the use of vehicle load spatiotemporal data for more accurate analysis such as fatigue research.

An innovative and cost effective IOT system for character recognition of vehicle license plates

An innovative and cost effective IOT system for character recognition of vehicle license plates

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.

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.

Enhancing Automated Vehicle License Plate Recognition with YOLOv8 and EasyOCR

This research focuses on the development of an automatic system for vehicle license plate recognition using YOLOv8, EasyOCR, and CNN methods for object classification. The main issue raised is the need for an accurate and efficient system for recognizing vehicle license plates in real-time in dynamic environments, especially in urban areas with high traffic levels. The method used in this study involves resizing the input image to 416x416 pixels to standardize the data, analyzing the YOLO architecture that divides the image into a 7x7 grid, and using the Convolutional Neural Network (CNN) algorithm for feature extraction and object classification. Object detection uses the YOLOv8 method which is tasked with recognizing license plates using a previously trained YOLO (pretrained model) model then implemented and tested using video with 4k quality to ensure its effectiveness in detecting vehicle license plate objects, followed by the Optical Character Recognition (OCR) process with the EasyOCR method to read text on license plates and tested to ensure its effectiveness in reading characters on license plates vehicle number. The purpose of this research is to develop a system that can improve accuracy and efficiency in vehicle license plate recognition. The results show that the accuracy, precision, recall and F1-Score for object detection reach 100% and the average percentage of detected text conformity is 74.66%, which shows that this system is reliable in real applications and contributes to the development of automatic license plate recognition technology.

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

ANALYSIS OF THE CHARACTERISTICS AND PARKING NEEDS OF RATU ZALECHA GENERAL HOSPITAL

Population growth in Indonesia is increasing so that the need for health care is also increasing. With this increase in growth, health facilities must also be improved, as is the case with the City of Martapura which already has various health facilities. One of them is Ratu Zalecha Regional General Hospital. With the increasing need for health, parking facilities must be improved as well. The solution to minimize this problem is to do a parking analysis in that area. Things that need to be analysis are parking characteristics, parking needs, parking forecasting. This study aims to determine the characteristics of parking, needs and forecasting of parking at Ratu Zalecha Regional General Hospital. The method used in this study was to conduct a survey in the field for 2 days, namely on Tuesday and Saturday, to get the number of parked vehicles and to record the license plates of vehicles entering and leaving.From the results of the study it was possible to produce the highest volume of motorcycles of 424 vehicles and cars of 152 vehicles, the highest accumulation of motorcycles was 134 and cars of 48 vehicles. The highest motorbike parking duration was 4,794 hours/vehicle and the highest car parking time was 4,939 hours/vehicle. Keywords: Characteristics of Parking, Parking Needs, Regional General Hospital Ratu Zalecha

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%.

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.

Studi Evaluasi Analisa Kebutuhan Lahan Parkir Pasar Tradisional (Studi Kasus: Pasar Desa Sugio)

Sugio Village Market is a traditional market located in Sugio Hamlet, this market is the only market in Sugio sub-district therefore this market is always crowded with people who want to shop for basic necessities every day. Market activity that causes an increase in the volume of vehicles entering and leaving the sugio market at certain hours makes it difficult for some vehicles to find a place to park. The method used in this study is a field survey by recording vehicle license plates in and out of the parking area. From the vehicle data is then analyzed to obtain accumulation, duration, parking volume, parking turnover rate, parking index and parking space needs. The results of this research were that the highest parking accumulation for motorbikes was 29 vehicles, while the highest accumulation for cars was 9 vehicles, the highest parking volume for motorbikes was 118 vehicles and the highest parking volume for cars was 12 vehicles, the highest average parking duration for bicycles. motorbikes are 1.91 hours/vehicle while for cars it is 3.5 hours/vehicle, the maximum parking capacity which can accommodate motorbikes is 204 vehicles/hour and to accommodate cars is 3 vehicles/hour, the highest parking index for motorbikes of 1.56 and the highest parking index for cars is 3.5, the highest parking turnover rate for motorbikes is 0.549 vehicles/srp/hour and the highest parking turnover rate for cars is 0.400 vehicles/srp/hour, for parking space needs additional area is needed land of 31.5 m2 for parking on the east side and 19.85 m2 for parking for motorbikes on the south side, while for cars an additional parking area of 113.75 m2 is needed in the area on the south side.