Vehicle Number Plate Research Articles

Effective Air Pollution Monitoring System for Eco Balance

Air pollution in urban areas is increasingly driven by vehicle emissions, which release harmful gases include such things as carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons (HC), and particulate matter (PM). These pollutants pose significant risks to both environmental quality and public health, highlighting the urgent need for effective monitoring and control systems. This project presents the development of an automated Effective Air Pollution Monitoring System for Eco Balance, designed to detect exhaust gas levels in real-time as vehicles pass designated points. The system employs advanced sensors to monitor pollutant concentrations and utilizes a camera- based recognition system to capture the number plates of vehicles that exceed permissible emission levels. The collected data, including pollution metrics and vehicle details, is transmitted to the Regional Transport Office (RTO) for necessary enforcement actions. By promoting compliance with emission standards, this automated system aims to significantly reduce vehicle-related air pollution, fostering a cleaner and healthier urban environment

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.

TV DOCUMENTARY PRODUCTION "THE EXISTENCE OF FAKE NUMBER PLATES"

This research was conducted to find out more about how behind the making of fake plates and why people prefer to use fake number plate making services instead of making them from official institutions (samsat). This documentary film uses the field research method. Field Research is research conducted directly into the field in order to obtain complete and valid data. This research aims to find out the activities of buying and selling fake plates of motorised vehicle numbers in terms of Law Number 22 Year 2009 and Indonesian law in Tangerang City. In addition, it is also to find out the process of buying and selling vehicle number plates because the problem is that the object (Fake Motor Vehicle Number Plates) being traded can be detrimental to the buyer because there is article 68 in the Law of the Republic of Indonesia Number 22 of 2009 concerning road traffic and transportation which prohibits the use of these goods. Based on the results of documentary production, it is found that the reason people use the service of making paslu number plates is because the original plate is damaged, avoiding the police to get a ticket, and also the reason for avoiding bureaucracy related to Foreign Citizens (WNA).

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.

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

Vehicle Number Plate Recognition

Recognizing vehicle number plates is a difficult but much needed system. This is very useful for automating toll booths, automated signal breakers identification, finding out traffic rule breakers and to detect stolen vehicles. The developed system consist of a Raspberry Pi based vehicle number plate recognition system that automatically recognizes vehicle’s number plate using image processing. The system uses a digital camera interfaced to a Raspberry pi processor. A rear image of vehicle is captured and processed using various algorithms. The system constantly processes incoming camera footage to detect any trace of number plates. On sensing a number plate in front of the camera, it processes the camera input and extracts the number plate part from the image. The image is extracted using OCR. And by using CNN techniques, the number is extracted from the image. The system then displays the extracted number. The implemented system consist of a fully functional vehicle number plate recognition system using Raspberry Pi considering success rate and processing time as parameters. The developed system can be used for security purpose in housing societies to monitor the entry/exit of authorized vehicles. It is observed that the developed system successfully detects and recognizes the vehicle number plate on real time images. The system accuracy is about 80%

Enhancing Automatic Vehicle Number Plate Recognition Systems: A Multidimensional Approach to Improve Efficiency and Applicability

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

License Plate Detection Based on Open CV

In this project a new approach of automatic vehicle number plate recognition in National Highways and this approach enables the vehicle to automatically pass the NH roads by using object detection, 3D environment construction, virtual line generation, path planning. The system is to monitor the vehicles that are entering and going out of the NH roads. All vehicles have their own unique license plate number, so the abstraction of plate number plays a major role in this system. The vehicle number plate is taken by the tool like machine learning based camera which is placed at the road junctions. The captured image will be processed by the automatic number plate recognition using OCR (optical character recognition) algorithm, here we using Gaussians filters for remove the blueness of the image using a Gaussian kernel suppresses only high-frequency spatial information. Then the entry and exit vehicle number should be recorded. A database is created with the vehicle number. This method is an efficient way of recognizing the vehicle number plate and strengthen the security system. In case of centralized receiver all the entrance records are kept in storage and it will check the presence of theft vehicle entering into the NH roads, it will be noticed and capture the vehicle image and vehicle information’s will be send to authorized person using IMAP (Internet Message Access Protocol). The OCR technique is used to identify the number. This system has many advantages like theft detection, less man power, user friendly, vehicle logging and less processing time.

Helmet Detection

Currently, there is a prevailing issue concerning the practice of riding motorcycles without helmets. Despite legislation mandating helmet usage for public safety reasons, there remains a significant number of individuals who disregard this requirement, leading to an alarming increase in accidents and fatalities. To mitigate this problem, we propose a method for the automated detection of helmetless motorcyclists through surveillance systems. Our approach utilizes the YOLO deep learning framework for detecting individuals without helmets, while a SM algorithm is employed recognition. This process involves several stages, including vehicle classification, pre-processing, and number plate identification, using images or videos captured by surveillance cameras. Implementing such a system is crucial for enhancing road safety and preventing accidents. By automatically identifying helmetless riders and extracting their vehicle numbers, law enforcement agencies can efficiently apprehend violators. This data can then be utilized to impose fines on repeat offenders, thereby reinforcing compliance with helmet regulations and reducing the occurrence of preventable accidents

Traffic Violation Detection System

Travel convenience is impacted by a number of factors, including the condition of the road, traffic, length of trip, accidents, speed, etc. The daily increase in accident rates poses the biggest concern in Sri Lanka. These events not only result in fatalities but also increase the nation's financial losses. Traffic congestion is brought on by road users' lack of discipline and sentiments, which may result in traffic offenses. The goals of this research are to curb any traffic infractions as well as reduce corruption and nepotism on the road. This system was developed as separate three models namely edge server, fine management system and centralized server. Edge server developed in python is to detect traffic violations such as parking, single line cutting, double line cutting, one way and u turn in real time according to the changing rules and constraints configured by the traffic police admin via the fine management system which supports a configurable platform designed using canvas designing tools. Upon edger server identifying violations, the functions in the centralized server responsible to recognize the number plate of the violated vehicle will be triggered and send a sms to driver for further fine management and payment supported by the fine management system. The mobile application controlling server is responsible to track and inform the violations to nearest police officer in any case the system could not rectify the vehicle number plate.

Enhanced Surveillance: Developing a Computer Vision-based CCTV Footage Analysis Tool for Improved Vehicle Number Plate Recognition

Abstract: Smart parking systems serve as integral components in augmenting the efficiency and sustainability of modern urban environments. Nonetheless, prevailing systems predominantly rely on sensors to monitor parking space occupancy, entailing substantial installation and maintenance costs while offering limited functionality in tracking vehicle movement within parking lots. To confront these challenges head-on, our solution introduces a multistage learning-based approach, capitalizing on the existing surveillance infrastructure within parking facilities and a meticulously curated dataset of Saudi license plates. Our approach amalgamates YOLOv5 for license plate detection, YOLOv8 for character detection, and a novel convolutional neural network architecture tailored for enhanced character recognition. We illustrate the superiority of our approach over single-stage methods, achieving an impressive overall accuracy of 96.1%, a notable advancement from the 83.9% accuracy of single-stage approaches. Furthermore, our solution seamlessly integrates into a web-based dashboard, facilitating real-time visualization and statistical analysis of parking space occupancy and vehicle movement, all accomplished with commendable time efficiency. This endeavor underscores the potential of leveraging existing technological resources to bolster the operational efficiency and environmental sustainability of smart cities. By harnessing the power of machine learning and computer vision, our work exemplifies a paradigm shift towards smarter and more adaptive urban infrastructure

Smart Traffic Surveillance: Identifying Helmet-Less Riders and License Plate Recognition with YOLO and OCR Integration using Deep Learning

Currently, there are a number of issues with Indian traffic laws that can be resolved in a variety of ways. Riding a motorbike without a helmet is against the law and has increased the number of accidents in India. The current method mostly uses CCTV records to monitor traffic offences. When a rider is not wearing a helmet, traffic officers must zoom in on the licence plate and look into the frame where the infraction is occurring. However, because there are more and more traffic infractions and motorcycle ridership every day, this will take a lot of labour and time. What if there was a technology that would automatically search for traffic violations such as riding a motorbike without a helmet? hence, would automatically retrieve the number plate number of the bike. This study has been effectively completed by recent research using KNN, DNN-Classifier, etc. However, there are limitations to these works in terms of speed, accuracy, or efficiency in object identification and classification. The goal of this research project is to develop a non- helmet rider detection system that can automatically identify and retrieve a vehicle's number plate number when a driver fails to wear a helmet. Three-level Deep Learning for Object Detection is the key idea at play here. The items found are a human, a motorcycle at level one using YOLO, a helmet at level two using YOLO, Licence plate at the last level using YOLO. Next, OCR is used to obtain the number plate registration number. All of these methods—especially the section where the number plate number is extracted—are subject to predetermined limitations and conditions. Because video is the work's input, speed of execution is essential. We have developed a comprehensive algorithm for both number plate number extraction and helmet detection using the above-mentioned approaches.

Detection of Vehicle Number Plate and Speed Using Machine Learning

In an era marked by a significant upsurge in vehicular traffic and the resultant increase in road accidents, the need for advanced traffic management systems is paramount. This paper presents an innovative solution, "Detection of vehicle number plate and speed using machine learning" which aims to enhance road safety by addressing the persistent problem of over-speeding. The project leverages state-of-the-art technologies, including machine learning, deep learning, and computer vision, to develop a smart system that can detect speeding vehicles, recognize license plates, and record crucial data related to speed limit violations. The primary motivation for this endeavor is the escalating rate of road accidents, particularly in India, driven by speeding in areas susceptible to accidents. The system employs image segmentation, corner detection algorithms, filtering algorithms, and automatic vehicle plate recognition techniques to achieve its objectives. By using machine learning and deep learning models, the system accurately identifies license plates, segments characters on these plates, and recognizes them. In summary, this project represents a significant advancement in road safety and traffic management. By deploying an intelligent system that can proactively detect over-speeding vehicles and maintain comprehensive records, it strives to create a safer and more responsible driving environment, ultimately working towards the prevention of accidents and saving lives. Key Words: Automatic Number Plate Recognition, Vehicle Speed Detection, Machine Learning, Computer Vision, Traffic Management

Safety measures in the workplace: a case study of Federal Road Safety Corps (FRSC) vehicle plate manufacturing plant, Ojodu – Lagos, Nigeria

Introduction: Hazards associated with vehicle number plate manufacturing processes in developing countries have not been well elucidated. There is a need to investigate hazards in the vehicle number plate manufacturing plant. The study aimed to identify self-reported hazards in the vehicle plate manufacturing factory, explore their effect on workers’ health and recommend mitigation strategies. Methods: A descriptive cross-sectional study of workers in the vehicle plate manufacturing plant was done to identify hazards, protective measures and self-reported effects on workers’ health. Biochemical analysis of blood samples was also done and outliers were noted. ANOVA test was performed to determine differences in mean values of selected biochemical parameters. A p-value of less than 0.05 was considered statistically significant. Results: Exposure to toxic chemicals 38(69%), excessive noise 36 (65%) and heat 28 (51%) were the major hazards in the factory. Major health problems were stress and fatigue 41 (71%), skin disorders 28 (51%), respiratory problems 18 (33%) and hearing loss 16 (29%). More than one quarter, 17 (30 %) of the workers admitted not using personal protective equipment (PPE) regularly. Periodic medical tests were not done for 13 (24%) of the workers. Potassium (3%), Urea (10%), Creatinine (7%), Aspartate transaminase (18%) and Alanine transaminase (8%) were elevated among the workers but no significant association could be established between elevated levels and work post. Conclusion: Workers in the plate number manufacturing plant were mostly exposed to toxic chemicals and noise. Elevated electrolyte levels cut across departments. There is a need for better enforcement of safety rules, and periodic medical examinations should be conducted more consistently.

Automatic Parking Management System and Parking Fee Collection Based on Number Plate Recognition

This research paper dives into the complexities of a programmed stopping framework and electronic stopping expense assortment, principally founded on vehicle number plate acknowledgment. The essential goal is to plan and carry out a programmed stopping framework that essentially improves the accommodation and security of public parking areas. Additionally, it aims to facilitate parking fee collection without the complexities associated with magnetic cards. The proposed automatic parking system boasts several key features. First and foremost, it minimizes human interaction by eliminating the need for magnetic cards and associated devices. Users can seamlessly enter and exit parking lots without physical tokens, enhancing overall convenience. Furthermore, the system incorporates a parking guidance feature that directs users to available parking spaces. Real-time information about vacant spots ensures efficient utilization of parking areas. The heart of the system lies in its number plate recognition capabilities. The significance of this research extends beyond parking management. It contributes to ongoing efforts to automate and digitize essential services, making them more accessible and user-friendly. By embracing such technological advancements, cities can enhance user experiences, improve efficiency, and pave the way for a future of automated systems in public services.

Automated Petrol Dispensing System with Smart Card and RFID Integration

Today, the majority of petrol stations employ microcontrollers to manage the pump operations, display controls, and flow measurement, but human interaction is still needed for payment collection. This project aims to automate the petrol dispensing process, eliminating the need for workers to handle payments. Users are provided with a rechargeable smart petrol card, which allows them to access petrol. When the card is placed in the reader, the microcontroller reads the data and prompts the user to enter the required liters of petrol on an LCD screen via a keypad. The system checks the card balance, dispenses the petrol if sufficient funds are available, and signals completion with a buzzer. Additionally, the system includes vehicle monitoring using RFID readers to scan vehicle number plates, alerting authorities if a vehicle is linked to a crime via SMS. A smart phone app tracks vehicle locations continuously.

E-Challan Based Intelligent Assistance System for Smart City

Traffic management and enforcement of road safety regulations have become increasingly crucial in modern urban environments, as the number of vehicles on the roads continues to surge. With the rise in traffic violations, particularly signal disobedience, there is a pressing need for innovative solutions that can efficiently detect and penalize offenders while reducing the burden on law enforcement personnel. The E-Challan System harnesses the power of cutting-edge computer vision techniques, most notably Automatic Number Plate Recognition (ANPR), to accurately capture and decipher vehicle number plates in real- time. High-resolution cameras strategically positioned at traffic junctions and signal intersections actively monitor passing vehicles, automatically detecting those that violate traffic signals. Upon successful identification of a signal violation, the E-Challan System promptly generates an electronic challan, or e-challan, containing detailed information about the violation, such as the date, time, and location of the incident. This comprehensive documentation serves as indisputable evidence of the offense and provides an essential legal foundation for imposing fines on violators. with an Auto Debit Fine mechanism.

Advantages of Automated License Plate Recognition Technology

Automatic Number Plate Recognition (ANPR) is a technology that can be utilized by mall and movie theater parking management as well as toll booths on roads, expressways, etc. to expedite the toll collection process. Automated systems to retain vehicle information are becoming more and more important as the number of vehicles rises. It is imperative that communication be prioritized in order to effectively manage traffic and reduce crime. Number plate recognition is a dependable technique for automatic vehicle identification. With current algorithms, which are based on the concept of learning, developing adequate outcomes takes a long time and a lot of experience. An innovative use of machine learning is Automatic Number Plate Detection, which recognizes photos and transforms them into text. Using picture segmentation, the system locates, picks up, and extracts the vehicle's image and number plate. Afterwards, optical character recognition technology is used to identify characters in the retrieved image. This technology is used in military zones, apartment buildings, and traffic surveillance.

An Efficient Deep Learning Approach for Automatic License Plate Detection with Novel Feature Extraction

In the domain of traffic management, road toll collection, and parking lot systems, vehicle number plate detection and identification play a pivotal role. Unlike conventional methods that treat license plate detection and character recognition as separate tasks, the system simultaneously addresses both challenges within a single neural network. Our Proposed methodology capitalizes on the efficiency and accuracy of the one-stage object detection algorithm known as YOLO (You Only Look Once) to locate license plates under diverse and challenging conditions. To augment the quality of input images with low resolution or poor clarity, we employ super-resolution generative adversarial networks (SRGANs). The image enhancement process substantially improves the visual quality of captured license plate images, facilitating more precise character recognition. Quantitative assessment of propounded system reveals compelling results. The license plate detection component achieves an outstanding average accuracy rate of 98.5%, surpassing previous methods by 15.2%. This comprehensive approach not only reduces the dependency on manual labour but also elevates processing precision. It seamlessly integrates into existing transportation infrastructure, resulting in heightened operational efficiency, reduced traffic congestion, and enhanced security measures. The rapid evolution of neural networks and deep learning techniques has streamlined the deployment of such applications, revolutionizing the field of traffic monitoring and management with unprecedented ease and precision. One-stage object detector, widely referred to as YOLO, is used to find licence plates in difficult circumstances.

Vehicle number recognition by using existing general surveillance cameras

India has a lot of cars because it is the densest-populated country in the world. Therefore, it's important to use a traffic management system to detect vehicles accurately. It detects a vehicle plate image from a camera and extracts the number. It's an embedded system that recognizes license plates in real-time. A real-time embedded system called Vehicle Plate Number Recognition (VNPR) recognizes license plate numbers automatically in this work using VGG16 algorithm. When a vehicle enters the entrance by the gate of the institution or highly restricted areas, the system records and captures video and recognizes the number plate. It will automatically recognize the license plate and search the data of the owner's vehicle and their challan details.