Vehicle Plate Research Articles

The Quality of Service of Vehicle Plate Online Registration in Cambodia: Assessment and Perspective of Online Customers

Limited evidence is presented for the evaluation of a particular online service, especially in developing countries. Cambodia introduced an online vehicle plate registration in 2017. Since then, there have not been any research studies on the quality of this public online service after the implementation. This research has two main aims: (1) to describe the quality of online vehicle plate registration and (2) to present citizens' perspectives who have experienced this online service provided by the Ministry of Public Works and Transport (MoPWT). The research employs a mix-method combining online questionnaires (100) and in-depth interviews (10) to answer the research questions regarding website design, reliability, citizen support, trust, and accessibility. The average score for all components and overall service quality is 2, suggesting that people perceive the service as having low quality. The results strengthen the influence of reliability and citizen support on the people’s perception of service quality as claimed by researchers. However, it contradicts the previous research in that website design though not well appreciated does not have much influence on the quality of service as claimed. The research also indicates some issues with the online system. Building more trust, providing better citizen support in time, and finally reducing the chances of corruption are components of services quality that should be improved to increase the number of people using the online service and increase citizen satisfaction.Online Service; Service Quality; Customers; Cambodia; Perspective; Citizen Support; Reliability

A vehicle plate recognition system based on deep learning algorithms

A vehicle plate recognition system based on deep learning algorithms

EXPLOITABLE EDGE ANALYSIS FOR FREE FLOW VEHICLE PLATE LOCALIZATION

In Malaysia, vehicle recognition system (VRS) such as vehicle plate recognition and counting, is rapidly growing and applied in many areas such as to identify vehicle identities for the law enforcement by authorities and electronic toll collection by highway agencies. Uncovering the region of interest in chaotic illumination environment at free flow road makes localizing license plate is critical process of VRS. Available edge vertical projection claimed to be robust to illumination, however, it tends to create false edges and sensitive to noises that can hinder the recognition performance. Thus, this research aims to propose a license plate localization method that based on exploitable edge analysis.comprising four main steps, namely pre-processing, rectangular blob searching, analysis and the vertical rectangular blobs projection. It calculates total and exploit edge information at y axis of the image. The proposed method is then tested on the European number plate datasets i.e. Baza Slika which contains about 167 vehicle images and Ondrej which contains about 97 vehicle images. The experimental results show that the proposed method outperforms the Ondrej method by obtaining accuracy of 95% on Baza Slika dataset and slightly lower by an accuracy of 91% on the Ondrej dataset. Then, the proposed method tested on the Malaysia vehicle dataset namely Tol Sungai Long data set which contains about 584 images of different illumination conditions, i.e. 297 images in the morning, 140 images in the afternoon and 147 images in the night. The proposed method outperforms other approaches with accuracy of 91.24%, 93.57% and 75.51% in the morning, evening and night respectively.

An efficient robust method for accurate and real-time vehicle plate recognition

An efficient robust method for accurate and real-time vehicle plate recognition

Automatic License Plate Recognition Using Optical Character Recognition

Automatic plate recognition is useful for everyday life, for example for law enforcement, traffic control, access to restricted areas, E-Tolls, or for checking in parking areas. The police number plate or TNKB is the main identity of a motorized vehicle that is roadworthy or operating on the highway. Thus, we can use the license plate to identify the vehicle owner. This study aims to recognize vehicle number plates using an Android smartphone. Using a camera on a handphone to scan the plate image, and it is processed to find out the vehicle owner’s information from the numbered plate being scanned. This study also uses Optical Character Recognition in processing images scanned by the camera where the steps taken are image acquisition, pre-processing, segmentation, normalization, feature extraction and recognition. The results show that the camera on an Android smartphone can be used to read and display information on number plate owners and also show that vehicle plate recognition using Optical Character Recognition based on the tests conducted shows the percentage of successful character recognition on motorized vehicle plates by 75% while the characters are equal to 97.36%.

Scale-Invariant Multidirectional License Plate Detection with the Network Combining Indirect and Direct Branches.

As the license plate is multiscale and multidirectional in the natural scene image, its detection is challenging in many applications. In this work, a novel network that combines indirect and direct branches is proposed for license plate detection in the wild. The indirect detection branch performs small-sized vehicle plate detection with high precision in a coarse-to-fine scheme using vehicle–plate relationships. The direct detection branch detects the license plate directly in the input image, reducing false negatives in the indirect detection branch due to the miss of vehicles’ detection. We propose a universal multidirectional license plate refinement method by localizing the four corners of the license plate. Finally, we construct an end-to-end trainable network for license plate detection by combining these two branches via post-processing operations. The network can effectively detect the small-sized license plate and localize the multidirectional license plate in real applications. To our knowledge, the proposed method is the first one that combines indirect and direct methods into an end-to-end network for license plate detection. Extensive experiments verify that our method outperforms the indirect methods and direct methods significantly.

Thermal analysis on car brake rotor using cast iron material with different geometries

The vehicle plate brake rotor is a pivoting gadget. Slowing down is a cycle which changes over the dynamic energy of the vehicle into mechanical energy which must be scattered as warmth. This paper presents the examination of the temperature and warmth motion at the plate interfaces utilizing an itemized 3-dimensional limited component model of a genuine vehicle brake rotor. Rotor disc are made utilizing CATIAV5R20 and recreated utilizing ANSYS R 20 which depends on the (FEM) analysisHere take cast iron material and seen that the maximum temperature and heat fluxes values for all three geometries like Solid rotor drilled rotor and drilled & slotted rotor respectively like are 589.48 °C, 555.67 °C and 539.97 °C and 2.9e5 w/mm2, 4.96e5 and 5.04e5w/mm2. Here we can clearly observe that drilled & slotted rotor geometry has very high value of heat flux compare to other rotor geometries. Thermal Analysis on car brake rotor is done by using and simulate the different parameters of various materials. The main findings point to experimented the potential investigation of car brake rotor to give best working performance. Two sorts of brake social occasions are being utilized, drum brakes and circle brakes. Drum brakes produce slowing down force on the internal surface of brake drum, the force associated is radially outward to contact the drum. The wheel-wheel studs are associated with the drum, as such when the drum stops the wheel similarly stops. It is important to acquire rough answers for issue instead of accurate shut discover arrangement. It is unimaginable to expect to get investigative numerical answers for some building issues.

Deep-Learning-Based Approach for Iraqi and Malaysian Vehicle License Plate Recognition.

Recognizing vehicle plate numbers is a key step towards implementing the legislation on traffic and reducing the number of daily traffic accidents. Although machine learning has advanced considerably, the recognition of license plates remains an obstacle, particularly in countries whose plate numbers are written in different languages or blended with Latin alphabets. This paper introduces a recognition system for Arabic and Latin alphabet license plates using a deep-learning-based approach in conjugation with data collected from two specific countries: Iraq and Malaysia. The system under study is proposed to detect, segment, and recognize vehicle plate numbers. Moreover, Iraqi and Malaysian plates were used to compare these processes. A total of 404 Iraqi images and 681 Malaysian images were tested and used for the proposed techniques. The evaluation took place under various atmospheric environments, including fog, different contrasts, dirt, different colours, and distortion problems. The proposed approach showed an average recognition rate of 85.56% and 88.86% on Iraqi and Malaysian datasets, respectively. Thus, this evidences that the deep-learning-based method outperforms other state-of-the-art methods as it can successfully detect plate numbers regardless of the deterioration level of image quality.

Vehicle plate localization and extraction based on hough transform and bilinear operations

<p>In general, the extraction of the vehicle plate is a previous step of plate recognition, and it actively studied for several decades. Plate localization is used in various security and traffic applications. In this paper, the proposed method is efficient to localize a plate for the multinational countries. The proposed method consists of three levels. The first level is the preprocessing<strong> </strong>that contains several steps. The digital camera capture images have been taken about twenty meters from the car with zooming two to three meters. Images are resampled using the zooming technique (bilinear interpolation) that makes the dimension of image (1024 x 768) pixels. The resampled images are resized to (940x 730). These images are converted to grayscale (green channel), and the bilateral filter is applied for removing noise. The second level is plate detection that adopts morphological operations, image subtraction, and vertical edge detection (Sobel). At the last Connected component analysis and Hough transform are used. The third level is the Deskew plate that notifies the plate may skew so that Hough transform is used to detect the largest line segment. Then, the images are rotated using bilinear interpolation. About 860 images are tested for different countries (Iraq, Belarus, Armenia, Hungary), and the accuracy is 98.99 % for extraction of the plate and 100% for the Deskew plate. Thus, the proposed system shows high efficiency in achievement.</p>

Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to identify and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully detects LP and recognizes multi-style Arabic characters with rates of 96% and 97.872% respectively under different conditions.

VideoDP: A Flexible Platform for Video Analytics with Differential Privacy

AbstractMassive amounts of videos are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps). Unfortunately, privacy leakage in the blurred video cannot be effectively bounded, especially against unknown background knowledge. In this paper, to our best knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Finally, we conduct experiments on real videos, and the experimental results demonstrate that VideoDP can generate accurate results for video analytics.

Development of Iraqi License Plate Recognition System based on Canny Edge Detection Method

In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the road in all the sections of the country. Vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the developing system is consist of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny Edge detection algorithm, Connect Component Analysis (CCA) have been exploited for segmenting characters. Finally, a Multi-Layer Perceptron Artificial Neural Network (MLPANN) model is utilized to recognize and detect the vehicle license plate characters, and hence the results are displayed as a text on GUI. The proposed system successfully identified and recognized multi_style Iraqi license plates using different image situations and it was evaluated based on different metrics performance, achieving an overall system performance of 91.99%. This results shows the effectiveness of the proposed method compared with other existing methods, whose average recognition rate is 86% and the average processing time of one image is 0.242s which proves the practicality of the proposed method.

Deep corner prediction to rectify tilted license plate images

Skewness and obliqueness of vehicle plate images influence license plate recognition. The more tilted plate images are, the harder the recognition task is. To this end, if plate images are preprocessed to be aligned and rectified, the recognition performance would improve. We propose deep neural network models that can locate four corner plate positions, which can then be used to perform the perspective transformation that can be used to rectify plates. Such a transformation is called homography. The models consist of two sequential parts: a feature extraction part having convolution and a regression part with fully connected layers. The models are open in the sense that the feature extraction part can host other well-known models such as Mobilenet as long as they have the feature capture capability. We devise a loss function as the sum of Euclidean distance between predicted coordinates and ground truth and discuss image augmentation schemes. The experiment results show that the models with well-known object detection models are able to predict corner positions with relatively high precision.

The Implementation Of Sound Output Image Processing With Wireless Based On Control To Vehicle Identification

Vehicle license plate identification is a system that utilizes image processing techniques to identify vehicles automatically from the vehicle license plate image. This system can be applied in various applications such as tracking stolen cars in homes or parking lots, automatic parking ticket payments, and collecting traffic statistics. Parking space as a facility to park motorists’ vehicles to be more organized. Parking space is currently still done conventionally. The development of technology, conventional control systems developed into electronic access systems, one of which is a monitoring system. The monitoring system using CCTV is a monitoring device, but it is expensive at the cost of electrical, network installation and limited mobility of device placement. In this study a Raspberry Pi-based room control system was designed. Raspberry Pi to build inexpensive monitoring systems and electricity-saving technology. This study aims to create a remote-based IP camera control system wirelessly for processing image plates of vehicle sound output with Raspberry Pi. The system starts by capturing the vehicle license plate with a Raspberry Pi-based camera or webcam. The control system is created using a motion program, and plate image processing uses digital image processing techniques and the Optical Character Recognition (OCR) method. The results of the system research can identify a Raspberry Pi server-based vehicle plate. The result is the vehicle license plate or text and voice output with text to speech technology. The results of implementing image processing from nine plate tests are 90.278%.

License Plate Detection using OCR with KNN

This paper presents associate in nursing economical methodology for the detection of vehicle plate by accurately localizing number plates from pictures. A straight forward downscaling of image methodology is projected initial to boost the speed of vehicle plate localization while not reducing the detection performance compared thereupon victimization the first image. Moreover, a completely unique Line Density Filter (LDF) methodology is projected to identify desired regions and eventually reducing the realm to be analysed for vehicle plate detection. Also, a vehicle plate classifier supported SVMs victimization color prominence options employed to spot the vehicle plate from the image. For performance check, a dataset having thirty pictures taken from completely different scenes under different conditions is additionally used. The data set demonstrates that the projected methodology performs well in terms of accuracy and run-time potency.

Vehicle Monitoring System

In the fastmoving world, it is nearly impossible to check and verify the details of each and every vehicle manually. So, in order to untangle this problem, we came up with vehicle monitoring system. The prime idea of our project focusses on drawing out the vehicle registration number from the number plates captured using the camera and process the captured number plate image using various algorithms and eventually store the processed data. This data can be used for monitoring the vehicles in parking lots. Management of cars in parking lots requires a lot of human intervention. Parking lots have been installed with cameras, but none of them serve the purpose. They are just installed for video surveillance and none of them solve the problem. Previously, i.e. in the mini project part, we proposed an Automatic Vehicle Plate Recognition system, which captures the vehicle image, processes the number plate and gives out the details of the vehicle. With the help of this module, we propose a system which can be used in parking lots to monitor the vehicles entering and exiting the lot. We can even run a background check on vehicles using the number plate details and alert the police department in case if the vehicle is suspicious.

Proposal for Automatic Vehicle Number Plate Recognition System in POLIMAS

Automatic Vehicle Number Plate Recognition (AVNPR) system is an image processing technology in computer vision which captures the image of the vehicle and recognizes its number plate. Currently, the security and management of transportation system becomes an important key in controlled place such as campus area. With an increased number of vehicles, there is a need for vehicle recognition mechanism that is effective, affordable and efficient. The objective of this paper is to propose an efficient automatic vehicle identification system by recognizing the vehicle plate number. The system will be installed at the main entrance of POLIMAS to ensure that only the authorized vehicles can automatically enter the campus area. Once the vehicle is detected by the input sensors, AVNPR system will capture the image of vehicle plate number. An image is then extracted and investigated character segmentation by using optical character recognition (OCR). The method used for detection of a plate number is by pre-processing of the image and using a combination of Sobel edge detection and Laplacian edge detection techniques. Bounding Box technique is used to find the number plate and character segmentation. For character recognition, OCR method is used by using template matching to compare the segmented image with the template. The system is sustainable as the camera will only be switched on when a car is present. The propose system successfully detects and recognizes the vehicle number plate on real images. This system can also be used for security and traffic control.

Indonesian License Plate Detection Using Morphological Operation

Technological developments have enabled the use of vehicle number plate recognition in various fields such as parking systems, e-toll payments, handling motor vehicle theft cases, traffic violations and so on. Vehicle number plate characteristics in each country have characteristics that are not the same. Each country has a standard numbering system in its writing method, colour, language and size of the license plate, so that to take the research standards for license plate numbers from other countries is not effective. Special research is needed in accordance with the format of the number plate used by the country, in this case Indonesia. This research applies simple method, morphological operations in detecting vehicle plates in Indonesia. Morphological results will be segmented to get the image of the plate character separately. The test results show that the method applied can provide a maximum accuracy of up to 100% on taking plate images with a distance of 1.5 meters.

Development of Smart Plate Number Recognition System for Fast Cars with Web Application

Traffic law violation has been recognized as a major cause for road accidents in most parts of the world with majority occurring in developing countries. Even with the presence of rules and regulations stipulated against this, violators are still on the increase. This is due to the fact that the rules are not properly enforced by appropriate authorities in those parts of the world. Therefore, a system needs to be designed to assist law enforcement agencies to impose these rules to improve road safety and reduce road accidents. This work uses a Vehicle Plate Number Recognition (VNPR) system which is a real-time embedded system to automatically recognize license plate numbers. It provides an alternative means to VPNR using an open-source library known as openCV. The main aim of the system is to use image processing to identify vehicles violating traffic by their plate numbers. It consists of an IR sensor for detecting the vehicle. During testing, a minimum time was set for the sensor to detect the object which was recorded by the microprocessor. Once it was less than the set time, the camera was triggered to capture the plate number and store the image on the Raspberry Pi. The image captured is processed by the Raspberry Pi to extract the numbers on the image. The numbers on the capture imaged were viewed on a web page via an IP address. The system if implemented can be used to improve road safety and control traffic of emerging smart cities. It will also be used to apply appropriate sanctions for traffic law violators.

License plate recognition system based on principal component analysis and one-against-one multi-class support vector machine

The license plate number which identifies the original registered place of the vehicle, as well as the usage of each vehicle which belongs to a private entity, a government vehicle, and a commercial vehicle, is identified in this work. Vehicle plate detection by a camera that mounted on parking gate. Detection process starts with collecting data using the camera. This work consists two important parts, namely image processing and classification system, respectively. In the image processing, the plate image capture by the camera is extracted to the feature space which will use as the input to the classification system. The classification system is then will identify the extracted input to the plat recognition identification. The Fast Fourier Transform (FFT) and Support Vector Machine (SVM) are applied as feature extraction technique and classification methods, respectively. The results of the training and testing phase are 80% and 66%, respectively.