Vehicle Plate Research Articles

Implementation of You Only Look Once Version 8 Algorithm to Detect Multi-Face Drivers and Vehicle Plates

Implementation of You Only Look Once Version 8 Algorithm to Detect Multi-Face Drivers and Vehicle Plates

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.

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

A Robust License Plate Detection System Using Smart Device

The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license plate number, rendering existing license plate recognition systems from other countries incapable of effectively detecting license plates on Malaysian car plates. A traditional LPR system typically requires a high-quality camera and a powerful computer for costly and bulky processing. Nowadays, many smartphones come equipped with powerful processors and cameras. Android smartphones include various libraries for modifying hardware configurations such as the camera. This paper presents a robust method for detecting Malaysia's license plate number using a convolutional neural network (CNN). The CNN model from the pre-training process is imported to the Android device and tested in real-time in an on-road driving environment, resulting in an average recognition rate of 89.37%. A comprehensive Character Recognition Analysis is also presented to demonstrate the accuracy of each character. However, there is still room for improvement in recognizing the character Q.

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.

Study on the effect of annealing rate on the impact resistance of the H-ZLaF50 vehicle plate glass

The H-ZLaF50 glass has attracted much attention in the field of automotive glass applications due to its excellent mechanical properties. The effect of the annealing rate on the impact resistance of the H-ZLaF50 glass was studied. High-precision infrared stress meter was used to test the stress distribution of glass under different annealing rates and the influence of annealing rate on the stress distribution was found. Secondly, the impact resistance of the H-ZLaF50 glass samples was evaluated by the test method of electric falling ball impact resistance. The maximum impact energy of the H-ZLaF50 glass samples was 0.120 J. The results show that, compared with other annealing rates, the annealing rate of 1°C/min effectively improves the impact resistance of the H-ZLaF50 glass, which makes it more widely used in the automotive flat glass market.

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.

Persepsi Mahasiswa Ilmu Politik Angkatan 2021 Terhadap Jalan Alternative Batubara Yang di Janjikan Oleh Gubernur Jambi

Coal in Jambi is a problem that people continually complain about and causes traffic jams and damaged roads. Until the end of 2022, there are 51 coal mining companies operating in Jambi Province, spread across Sarolangun, Tebo, Batanghari and Muaro Jambi. The Jambi provincial government is preparing alternative coal transportation routes via land and water. This research uses quantitative research with a descriptive format, aimed at explaining, summarizing various conditions, various situations or various variables that arise in the community which is the object of the research based on what happens. Then bring to the surface the character or description of the condition, situation or variable. The results of observations from questionnaires that have been distributed by students hope that this alternative will reduce the level of traffic jams and the number of accidents that occur in the community. The Governor of Jambi, Al Haris, has made various efforts in alternative development in his province. Students hope that this alternative will reduce the level of traffic jams and accidents that occur in society. The Governor of Jambi, Al Haris, has made various efforts in alternative development in his province. One of them is controlling the use of coal transport vehicle plates to repair roads. Apart from that, the provincial government is also discussing the issue of coal transport and the construction of special roads for coal transport. The Governor also admits that his authority is limited in matters of coal transport, but various efforts have been made. carried out, including opening alternative roads and giving lambuk numbers to coal transport. Apart from that, Governor Al Haris also stated the ban on coal transport via national roads and the importance of the progress of Jambi City as a barometer for the development of Jambi province. However, despite this, we can also feel that currently the accident rate is still there.

A Smart Real-Time Parking Control and Monitoring System.

Smart parking is an artificial intelligence-based solution to solve the challenges of inefficient utilization of parking slots, wasting time, congestion producing high CO2 emission levels, inflexible payment methods, and protecting parked vehicles from theft and vandalism. Nothing is worse than parking congestion caused by drivers looking for open spaces. This is common in large parking lots, underground garages, and multi-story car parks, where visibility is limited and signage can be confusing or difficult to read, so drivers have no idea where available parking spaces are. In this paper, a smart real-time parking management system has been introduced. The developed system can deal with the aforementioned challenges by providing dynamic allocation for parking slots while taking into consideration the overall parking situation, providing a mechanism for booking a specific parking slot by using our Artificial Intelligence (AI)-based application, and providing a mechanism to ensure that the car is parked in its correct place. For the sake of providing cost flexibility, we have provided two technical solutions with cost varying. The first solution is developed based on a motion sensor and the second solution is based on a range-finder sensor. A plate detection and recognition system has been used to detect the vehicle's license plate by capturing the image using an IoT device. The system will recognize the extracted English alphabet and Hindu-Arabic Numerals. The proposed solution was built and field-tested to prove the applicability of the proposed smart parking solution. We have measured and analyzed keen data such as vehicle plate detection accuracy, vehicle plate recognition accuracy, transmission delay time, and processing delay time.

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.

Sistem Pemantauan Slot Parkir Menggunakan Sensor Ultrasonik JSN-SR04T dan Pengenalan Plat Nomor Kendaraan dengan ESP32-CAM

A parking slot monitoring system prototype has been produced using the JSN-SR04T ultrasonic sensor and vehicle license plate recognition with ESP32-CAM. The system was created to make it easier for motorists to monitor the availability of parking slots remotely by utilizing Telegram which is connected to the ESP32-CAM module and Arduino Mega 2560. The ultrasonic sensor detects the distance of vehicles entering or exiting the parking lot, then a servo motor opens the entry and exit bars. Vehicle users can monitor the availability of parking slots on the Telegram bot. In each slot there is an ultrasonic sensor that detects the distance so that it can find out whether there is a vehicle in the slot. The test results show that the ultrasonic sensor has an average error percentage of 0.65%. The parking barrier can be opened when the ultrasonic sensor detects the presence of a vehicle at the entrance or exit and the ultrasonic sensor can detect the presence of a vehicle according to the conditions in the slot. The system is equipped with vehicle license plate character recognition using the Optical Character Recognition (OCR) method. Through the ESP32-CAM module camera webserver, the OCR program can detect vehicle plates with the furthest distance of 140 cm using video resolution (400x296) and 300 cm using video resolution (1600x1200). The OCR program can read the vehicle plate characters correctly and the results for the number of characters obtained correspond to the license plate being tested, then the vehicle plate data is stored in the database

A vehicle license plate data access model based on the jump hash consistency algorithm.

The massive amount of vehicle plate data generated by intelligent transportation systems is widely used in the field of urban transportation information system construction and has a high scientific research and application value. The adoption of big data platforms to properly preserve, process, and exploit these valuable data resources has become a hot research area in recent years. To address the problems of implementing complex multi-conditional comprehensive query functions and flexible data applications in the key-value database storage environment of a big data platform, this paper proposes a data access model based on the jump hash consistency algorithm. Algorithms such as data slice storage and multi-threaded sliding window parallel reading are used to realize evenly distributed storage and fast reading of massive time-series data on clustered data nodes. A comparative analysis of data distribution uniformity and retrieval efficiency shows that the model can effectively avoid generating the cluster hotspot problem, support comprehensive analysis queries with various complex conditions, and maintain high query efficiency by precisely positioning the data storage range and utilizing parallel scan reading.

DESIGN AND BUILD VEHICLE PLATE DETECTION SYSTEM USING YOU ONLY LOOK ONCE METHOD BASED ON ANDROID

The method of collecting the vehicle data is conducted conventionally by gathering data from each region to be converted into single, raw information in the form of vehicle plates for all regions, to be processed on a computer and sent to the Central Bureau of Statistics. It is then transformed into a form of national data file that provides information on vehicle plates for the Indonesian people. This kind of data gathering method requires a lot of time and effort. Therefore, it is a concern for researchers to detect vehicle plates using image processing by utilizing the Android-based You Only Look Once method. The YOLOv4 technique is used because it processes image data directly with optimal performance in order to produce faster predictions. In its application, the researchers use Google Collaboratory to create models and Android Studio for android applications. At the same time, the parameters studied were precision, recall, F1 score, average IoU, and mAP. By using the "Vehicle Registration Plate" dataset, the ratio of which is 70% in training data and 30% in data validation, an accuracy of 77% is obtained with a detection time of 0.05 seconds, whereas the average accuracy value is 86.82%. Therefore, it can be concluded that this study has an optimized performance for detecting vehicle plates using the Android application.

Design of Automatic Number Plate Recognition System for Yemeni Vehicles with Support Vector Machine

Automatic Number Plate Recognition (ANPR) is an important tool in the Intelligent Transport System (ITS). Plate features can be used to provide the identification of any vehicle as they help ensure effective law enforcement and security. However, this is a challenging problem, because of the diversity of plate formats, different scales, rotations and non-uniform illumination and other conditions during image acquisition. This work aims to design and implement an ANPR system specified for Yemeni vehicle plates. The proposed system involves several steps to detect, segment, and recognize Yemeni vehicle plate numbers. First, a dataset of images is manually collected. Then, the collected images undergo preprocessing, followed by plate extraction, digit segmentation, and feature extraction. Finally, the plate numbers are identified using Support Vector Machine (SVM). When designing the proposed system, all possible conditions that could affect the efficiency of the system were considered. The experimental results showed that the proposed system achieved 96.98% and 99.19% of the training and testing success rates respectively.

IMPLEMENTATION OF IMAGE PROCESSING IN THE RECOGNITION OF OFFICIAL VEHICLE LICENSE PLATES

Vehicle license plates are identifiers used to uniquely identify vehicles. However, to identify vehicle license plates there are several problems encountered, namely the different formats of vehicle license plates that make license plate recognition more complicated, vehicle license plates often contain visually similar combinations of letters and numbers (for example the letter "O" and the number "0" or the letter "I" and the number "1"), . in poor lighting conditions license plates may not be clearly visible. To solve this problem, image recognition, image processing, and pattern recognition technologies can be used. The three technologies can be used to recognize characters on vehicle license plates, but cannot yet be used to recognize the colors contained on vehicle license plates. The purpose of this research is to identify and record vehicle license plate numbers quickly and accurately, monitor the presence of vehicles in a supervised area, assist in managing parking, reduce the need for human interaction in the vehicle identification process, The methods used to recognize motor vehicle plates are edge detection and character segmentation which involves image processing to detect the edges of the vehicle plate, followed by segmentation of individual characters in the plate. Another method used is optical character recognition which involves using an optical sensor to take an image of a vehicle plate, then using character recognition techniques to identify the numbers and letters on the plate. The result of this research is that the motor vehicle number recognition system can work in various lighting conditions and poor weather conditions and can monitor and control vehicles in the parking area. The finding obtained from this research is that no method has been used for color recognition on motor vehicle plates.

Desain Sistem Pendeteksi Plat Kendaraan Untuk Pengisian BBM Menggunakan Metode K-Nearest Neighbor (KNN)

This study aims to design and develop a vehicle plate detection system for refueling. Applications built using the K-Nearest Neighbor (KNN) method, this method is used to classify vehicle plates. This research will use the OpenCV module in the python programming language to recognize vehicle plates and carry out the pre-processing, segmentation, feature extraction, and classification stages of vehicle Image data. This system will utilize the K-Nearest Neighbor (KNN) method such as edge detection, perspective transformation, and pattern recognition to achieve optimal results. The test results show that the system is able to recognize with high accuracy various types of vehicle license plates under different lighting conditions and viewing angles, thus a system can be created and determined for each license plate to only be refueled once a day. When the vehicle has never been filled on that day, the system will display a green light which means refueling can be done. Whereas for vehicles that have filled in on that day, the system will display a red light which means the vehicle cannot fill up so it must wait the next day after the system is reset. This system will be able to accurately and real-time detect and identify the vehicle plates used in the refueling process. Therefore, the design of this system is expected to be the basis for increasing efficiency in the refueling industry as well as providing a basis for the development of further research in this field.

Proposed Relationship Marketing Strategy for Used Car Dealer

Indonesia has been one of Southeast Asia’s largest used car markets. Approximately 2.5-3 million used cars are sold annually. The increased competition in the automotive industry has heightened the emphasis on improving customer relationships. ABC Showroom (pseudonym) is a local used car dealer in Bukittinggi currently facing unsatisfactory sales performance and lacking relationship marketing strategy. The aim of this research is to formulate the relationship marketing strategy for ABC Showroom and find out whether the strategy differs amongst its customer persona. Internal analysis and external analysis are conducted to formulate the strategy. The internal analysis consists of marketing mix (7P) and STP analysis. The external analysis includes PEST analysis and competitor analysis. A quantitative approach using a survey is carried out to collect information on target market preferences regarding the proposed relationship marketing strategy, specifically personal selling, personalization, and after-sale service. The total of respondents obtained in this research is 240 respondents. The Two-step clustering analysis is conducted to build the customer persona of ABC Showroom. Analysis continued by conducting the Kruskal-Wallis test to find out if the proposed strategies differ amongst customer persona. Based on the result of the Kruskal-Wallis test, the proposed strategies do not differ amongst customer persona. Findings on internal analysis and external analysis are summarized with a SWOT matrix. Then the TOWS matrix is used to assist the author in formulating the relationship marketing strategy while tailoring it with the survey result. The relationship marketing strategies proposed includes improving salesperson performance by providing target and uniform, providing remarkable test drive experience, providing proactive and caring customer service, providing personalized offers and car products, and providing after-sales services such as warranty, repair, and maintenance service, car ownership transfer and vehicle plate transfer assistance, and car tax payment assistance.

Integrating MLOps and EEG Techniques for Enhanced Crime Detection and Prevention

The use of machine learning and artificial intelligence in crime detection has gained significant attention in recent years. This research paper explores the potential of MLOps techniques in identifying criminal behavior through the analysis of vehicle plate numbers, person detection, and object behavior detection. In this paper, we present a literature review of studies that have investigated the use of MLOps in crime detection and highlight the potential of these techniques to be used in criminal investigations. Specifically, we discuss the use of machine learning models for vehicle plate number recognition, person detection through image and video analysis, and object behavior detection through image and video analysis. We also present a framework for integrating MLOps techniques into criminal investigations, which involves a combination of data acquisition, data preprocessing, model development, model training and testing, and deployment. Additionally, we discuss the ethical implications of using MLOps techniques in criminal investigations and highlight the need for transparency and fairness in model development and deployment. The detection and prevention of criminal behavior is a critical issue for society. In recent years, there has been growing interest in the use of electroencephalogram (EEG) techniques to detect and predict criminal behavior. This research paper explores the potential of EEG techniques as a means of detecting crime by analyzing brainwave activity. Specifically, we examine the use of alpha and beta waves in identifying deceptive or abnormal behavior. We present a literature review of studies that have investigated the relationship between EEG signals and criminal behavior and highlight the potential of these techniques to be used in criminal investigations. Furthermore, we propose a framework for integrating EEG techniques into criminal investigations, which involves a combination of data acquisition, analysis, and interpretation. Finally, we discuss the ethical implications of using EEG techniques in criminal investigations and highlight the need for further research in this area.

Detecting Vehicle Numbers Using Google Lens-Based ESP32CAM to Read Number Characters

plates continues to increase. This research aimed to detect vehicle license plates using ESP32CAM and utilize photo text reading using Google Lens, which can be used online to retrieve numeric characters. The method approach was to connect Wifi connectivity to the ESP32CAM, which had been programmed to detect vehicle plates. Vehicle plates that have been detected and recognized were inputted into Google Lens to capture the resulting text from the ESP32CAM camera recording. The results of this study were that for 70 seconds, ten plate samples were obtained, which were 100% perfect in reading license plates on Google Lens, namely six plates and two plates read 90%, one plate read 60%, and one plate read 0%. The research conclusions obtained were ten samples, six samples with perfect readings, and one error sample because of the white plate color. Thus, the main objective was to obtain the results of the vehicle plate detection and retrieve the text from the recording results