Готові списки джерел за темами / LICENSE PLATE DETECTION SYSTEM

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Добірка наукової літератури з теми "LICENSE PLATE DETECTION SYSTEM"

Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "LICENSE PLATE DETECTION SYSTEM"

1

Ning, Yuan, Yao Wen Liu, Yan Bin Zhang, and Hao Yuan. "Extraction of License Plate Region in License Plate Recognition System." Applied Mechanics and Materials 441 (December 2013): 655–59. http://dx.doi.org/10.4028/www.scientific.net/amm.441.655.

Повний текст джерела
Анотація:
Extraction of License plate region is an important stage in the intelligent vehicle license plate recognition system. A practical license plate extraction algorithm based on edge detection and mathematical morphology is presented, the algorithm mainly consists of six modules: pre-processing, edge detection, binaryzation and denoising, morphology operation, filtration of connected regions, finding license plate region. From the experiments, the algorithm can detect the region of license plate quickly with 98% average accuracy of locating vehicle license plate region.
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2

Mudda, Avinash, P. Sashi Kiran, Ashish Kumar, and Venkata Sreenivas. "Vehicle Allowance System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1085–89. http://dx.doi.org/10.22214/ijraset.2023.50169.

Повний текст джерела
Анотація:
Abstract: License plate detection is an image processing technology that uses a license (number) plate for vehicle identification. The objective is to design and implement an efficient vehicle identification system that identifies the vehicle using the vehicle’s license plate. The system can be implemented at the entrance of parking lots, toll booths, or any private premises like colleges, etc. to keep records of ongoing and outgoing vehicles. It can be used to allow access to only permitted vehicles inside the premises. The developed system first captures the image of the vehicle’s front, then detects the license plate, and then reads the license plate. The vehicle license plate is extracted using image processing of the image. Optical character recognition (OCR) is used for character recognition. The system is implemented using OpenCV and its performance is tested on various images. It is observed that the developed system successfully detects and recognizes the vehicle license plate. To recognize License number plates using the Python programming language. We will utilize OpenCV for this project to identify the license number plates and the python py-tesseract for the characters and digits extraction from the plate. We will build a Python program that automatically recognizes the License Number Plate
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3

Wang, Rui Feng, Xiao Jin Fu, and Wei Xu. "License Plate Recognition System Design." Applied Mechanics and Materials 738-739 (March 2015): 639–42. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.639.

Повний текст джерела
Анотація:
The license plate recognition system is an important part of modern traffic management. application which is very extensive. In this paper, a method to achieve three main modules split from the image pre-processing, license plate location and character. Image pre-processing module of this article is to image gray and step by Roberts operator edge detection. License plate positioning and segmentation using mathematical morphology is used to determine the license plate location method, and then use the license plate color information of color segmentation method to complete the license plate parts division. This article is to research its main part and use MATLAB to do the image processing simulation.
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4

Bralić, Niko, and Josip Musić. "System for automatic detection and classification of cars in traffic." St open 3 (October 31, 2022): 1–31. http://dx.doi.org/10.48188/so.3.10.

Повний текст джерела
Анотація:
Objective: To develop a system for automatic detection and classification of cars in traffic in the form of a device for autonomic, real-time car detection, license plate recognition, and car color, model, and make identification from video.Methods: Cars were detected using the You Only Look Once (YOLO) v4 detector. The YOLO output was then used for classification in the next step. Colors were classified using the k-Nearest Neighbors (kNN) algorithm, whereas car models and makes were identified with a single-shot detector (SSD). Finally, license plates were detected using the OpenCV library and Tesseract-based optical character recognition. For the sake of simplicity and speed, the subsystems were run on an embedded Raspberry Pi computer.Results: A camera was mounted on the inside of the windshield to monitor cars in front of the camera. The system processed the camera’s video feed and provided information on the color, license plate, make, and model of the observed car. Knowing the license plate number provides access to details about the car owner, roadworthiness, car or license place reports missing, as well as whether the license plate matches the car. Car details were saved to file and displayed on the screen. The system was tested on real-time images and videos. The accuracies of car detection and car model classification (using 8 classes) in images were 88.5% and 78.5%, respectively. The accuracies of color detection and full license plate recognition were 71.5% and 51.5%, respectively. The system operated at 1 frame per second (1 fps).Conclusion: These results show that running standard machine learning algorithms on low-cost hardware may enable the automatic detection and classification of cars in traffic. However, there is significant room for improvement, primarily in license plate recognition. Accordingly, potential improvements in the future development of the system are proposed.
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5

Mahmood, Zahid, Khurram Khan, Uzair Khan, Syed Hasan Adil, Syed Saad Azhar Ali, and Mohsin Shahzad. "Towards Automatic License Plate Detection." Sensors 22, no. 3 (February 7, 2022): 1245. http://dx.doi.org/10.3390/s22031245.

Повний текст джерела
Анотація:
Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.
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6

Abdullah, Siti Norul Huda Sheikh, Muhammad Nuruddin Sudin, Anton Satria Prabuwono, and Teddy Mantoro. "License Plate Detection and Segmentation Using Cluster Run Length Smoothing Algorithm." Journal of Information Technology Research 5, no. 3 (July 2012): 46–70. http://dx.doi.org/10.4018/jitr.2012070103.

Повний текст джерела
Анотація:
For the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. In this paper, an automatic license plate detection system is proposed for Malaysian vehicles with standard license plates based on image processing and clustering. Detecting the location of license plate is a vital issue when dealing with uncontrolled environments and illumination difficulty. Therefore, a proposed algorithm called Cluster Run Length Smoothing Algorithm (CRLSA) was applied to locate the license plates at the right position. CRLSA consisted of two separate proposed algorithms which applied run length edge detector algorithm using kernel masks and 128 grayscale offset plus a three-dimensional way to calculate run length smoothing algorithm, which can improve clustering techniques in segmentation phase. Six separate experiments were performed; Morphology, CRLSA, Clustering, Square/Contour Detection, Hough, and Radon Transform. From those experiments, analysis based on segmentation errors was constructed. The prototyped system has accuracy more than 96%.
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7

Yaba, Hawar Hussein, and Hemin Omer Latif. "Plate Number Recognition based on Hybrid Techniques." UHD Journal of Science and Technology 6, no. 2 (September 1, 2022): 39–48. http://dx.doi.org/10.21928/uhdjst.v6n2y2022.pp39-48.

Повний текст джерела
Анотація:
Globally and locally, the number of vehicles is on the rise. It is becoming more and more challenging for authorities to track down specific vehicles. Automatic License Plate Recognition becomes an addition to transportation systems automation. Where the extraction of the vehicle license plate is done without human intervention. Identifying the precise place of a vehicle through its license plate number from moving images of the vehicle image is among the crucial activities for vehicle plate discovery systems. Artificial intelligence systems are connecting the gap between the physical world and digital world of automatic license plate detection. The proposed research uses machine learning to recognizing Arabic license plate numbers. An image of the vehicle number plate is captured and the detection is done by image processing, character segmentation which locates Arabic numeric characters on a number plate. The system recognizes the license plate number area and extracts the plate area from the vehicle image. The background color of the number plate identifies the vehicle types: (1) White color for private vehicle; (2) red color for bus and taxi; (3) blue color for governmental vehicle; (4) yellow color for trucks, tractors, and cranes; (5) black color for temporary license; and (6) green color for army. The recognition of Arabic numbers from license plates is achieved by two methods as (1) Google Tesseract OCR based recognition and (2) Machine Learning-based training and testing Arabic number character as K-nearest neighbors (kNN). The system has been tested on 90 images downloaded from the internet and captured from CCTV. Empirical outcomes show that the proposed system finds plate numbers as well as recognizes background color and Arabic number characters successfully. The overall success rates of plate localization and background color detection have been done. The overall success rate of plate localization and background color detection is 97.78%, and Arabic number detection in OCR is 45.56 % as well as in KNN is 92.22%.
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8

Molina-Moreno, Miguel, Ivan Gonzalez-Diaz, and Fernando Diaz-de-Maria. "Efficient Scale-Adaptive License Plate Detection System." IEEE Transactions on Intelligent Transportation Systems 20, no. 6 (June 2019): 2109–21. http://dx.doi.org/10.1109/tits.2018.2859035.

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9

Bhogale, Poonam, Archit Save, Vitrag Jain, and Saurabh Parekh. "Vehicle License Plate Detection and Recognition System." International Journal of Computer Applications 137, no. 9 (March 17, 2016): 31–34. http://dx.doi.org/10.5120/ijca2016908924.

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10

Adytia, Nico Ricky, and Gede Putra Kusuma. "Indonesian License Plate Detection and Identification Using Deep Learning." International Journal of Emerging Technology and Advanced Engineering 11, no. 7 (July 26, 2021): 1–7. http://dx.doi.org/10.46338/ijetae0721_01.

Повний текст джерела
Анотація:
Abstract— License plate is the unique identity of the vehicle, which serves as proof of the legitimacy of the operation of the vehicle in the form of a plate or other material with certain specifications issued by the police and contains the area code, registration number and validity period and installed on the vehicle. License plates are often used in automated parking systems and vehicle identification in case of traffic violations. So, it is necessary to build a system for detection and identification of license plates. The proposed license plate detection and identification system is divided into three main processes, namely license plate detection, character segmentation, and character recognition. The detection process uses transfer learning techniques using Faster R-CNN Inception V2. The segmentation process uses traditional computer vision with morphological operations and contours extraction. Then the character recognition process uses the MobileNet V2 transfer learning technique as an architecture for character classification. The recognition accuracy compared between MobileNet V2 and TesseractOCR shows that MobileNet V2 is superior with an accuracy rate of 96%, while Tesseract-OCR has a poor accuracy of 59%. Keywords— Deep Learning, Convolutional Neural Network, License Plate Detection, Character Segmentation, Character Recognition
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Більше джерел

Дисертації з теми "LICENSE PLATE DETECTION SYSTEM"

1

Colberg, Kathryn. "Investigating the ability of automated license plate recognition camera systems to measure travel times in work zones." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49048.

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Анотація:
This thesis evaluates the performance of a vehicle detection technology, Automated License Plate Recognition (ALPR) camera systems, with regards to its ability to produce real-time travel time information in active work zones. A literature review was conducted to investigate the ALPR technology as well as to identify other research that has been conducted using ALPR systems to collect travel time information. Next, the ALPR technology was tested in a series of field deployments in both an arterial and a freeway environment. The goal of the arterial field deployment was to evaluate the optimal ALPR camera angles that produce the highest license plate detection rates and accuracy percentages. Next, a series of freeway deployments were conducted on corridors of I-285 in Atlanta, Georgia in order to evaluate the ALPR system in active work zone environments. During the series of I-285 freeway deployments, ALPR data was collected in conjunction with data from Bluetooth and radar technologies, as well as from high definition video cameras. The data collected during the I-285 deployments was analyzed to determine the ALPR vehicle detection rates. Additionally, a script was written to match the ALPR reads across two data collection stations to determine the ALPR travel times through the corridors. The ALPR travel time data was compared with the travel time data produced by the Bluetooth and video cameras with a particular focus on identifying travel time biases associated with each given technology. Finally, based on the knowledge gained, recommendations for larger-scale ALPR work zone deployments as well as suggestions for future research are provided.
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2

Ning, Guanghan. "Vehicle license plate detection and recognition." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10157318.

Повний текст джерела
Анотація:

In this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process.

Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We tested our method on 200 real life images, captured on Chinese highways under different weather conditions and lighting conditions. And we achieved a detection rate of 100%.

After detecting license plates, alignment is then performed on the plate candidates. Conceptually, this alignment method searches neighbors of the bounding box detected, and finds the optimum edge position where the outside regions are very different from the inside regions of the license plate, from color's perspective in RGB space. This method accurately aligns the bounding box to the edges of the plate so that the subsequent license plate segmentation and recognition can be performed accurately and reliably.

The system performs license plate segmentation using global alignment on the binary license plate. A global model depending on the layout of license plates is proposed to segment the plates. This model searches for the optimum position where the characters are all segmented but not chopped into pieces. At last, the characters are recognized by another SVM classifier, with a feature size of 576, including raw features, vertical and horizontal scanning features.

Our character recognition results show that 99% of the digits are successfully recognized, while the letters achieve an recognition rate of 95%.

The license plate recognition system was then incorporated into an embedded system for parallel computing. Several TS7250 and an auxiliary board are used to simulate the process of vehicle retrieval.

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3

Luvizon, Diogo Carbonera. "Vehicle speed estimation by license plate detection and tracking." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/1380.

Повний текст джерела
Анотація:
CNPq
Sistemas de controle de velocidade são utilizados em vários países para fiscalizar o cumprimento dos limites de velocidade, prevenindo assim acidentes de trânsito. Muitos desses sistemas são baseados em tecnologias intrusivas que requerem processos de instalação e manutenção complexos, geralmente atrapalhando o trânsito. Neste projeto, propõe-se um sistema não intrusivo para estimativa da velocidade de veículos baseado em vídeo. O sistema proposto detecta veículos em movimento utilizando um detector de movimento otimizado. Aplicou-se um detector de texto especializado para localizar a placa dos veículos, a qual foi utilizada para seleção e rastreamento de pontos estáveis. Os pontos rastreados são então filtrados e retificados para remoção do efeito da perspectiva. A velocidade dos veículos é estimada comparando-se a trajetória dos pontos rastreados com dimensões conhecidas no mundo. Para os testes, utilizou-se aproximadamente cinco horas de vídeos em diferentes condições, capturados por uma câmera de baixo custo posicionada a 5,5 metros de altura. Os vídeos capturados contém mais de 8.000 veículos distribuídos em três pistas diferentes, com as velocidades reais para cada veículo obtidas a partir de um detector por laço indutivo. O detector de placas proposto foi comparado com três outros métodos no estado da arte e obteve os melhores resultados de performance para os nossos vídeos, com precisão (precision) de 0,93 e coeficiente de revocação (recall) de 0,87. A estimativa de velocidade dos veículos apresentou erro médio de -0,5 km/h, permanecendo dentro da margem de +2/-3 km/h, determinada por agências reguladoras em vários países, em 96,0% dos casos.
Speed control systems are used in most countries to enforce speed limits and, consequently, to prevent accidents. Most of such systems are based on intrusive technologies which require complex installation and maintenance, usually causing traffic disturbance. In this work, we propose a non-intrusive video-based system for vehicle speed estimation. The proposed system detects moving vehicles using an optimized motion detector. We apply a specialized text detector to locate the vehicle’s license plate region, in which stable features are selected for tracking. The tracked features are then filtered and rectified for perspective distortion. Vehicle speed is estimated by comparing the trajectory of the tracked features to known real world measures. For our tests, we used almost five hours of videos in different conditions, captured by a single low-cost camera positioned at 5.5 meters height. The recorded videos contain more than 8,000 vehicles, in three different road lanes, with associated ground truth speeds obtained from an inductive loop detector. We compared our license plate detector with three other state-of-the-art text detectors, and our approach has shown the best performance for our dataset, attaining a precision of 0.93 and a recall of 0.87. Vehicle speeds were estimated with an average error of -0.5 km/h, staying inside the +2/-3 km/h limit determined by regulatory authorities in several countries in over 96.0% of the cases.
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4

Gunaydin, Ali Gokay. "A Constraint Based Real-time License Plate Recognition System." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608195/index.pdf.

Повний текст джерела
Анотація:
License Plate Recognition (LPR) systems are frequently utilized in various access controls and security applications. In this thesis, an experimental constraint based real-time License Plate Recognition system is designed, and implemented in Java platform. Many of the available constraint based methods worked under strict restrictions such as plate color, fixed illumination and designated routes, whereas, only the license plate geometry and format constraints are used in this developed system. These constraints are built on top of the current Turkish license plate regulations. The plate localization algorithm is based on vertical edge features where constraints are used to filter out non-text regions. Vertical and horizontal projections are used for character segmentation and Multi Layered Perceptron (MLP) based Optical Character Recognition (OCR) module has been implemented for character identification. The extracted license plate characters are validated against possible license plate formats during the recognition process. The system is tested both with Turkish and foreign license plate images including various plate orientation, image quality and size. An accuracy of 92% is achieved for license plate localization and %88 for character segmentation and recognition.
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5

Kao, Kung-Chun, and 高孔君. "License Plate Detection on Autonomous Surveillance System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/64794522132045066604.

Повний текст джерела
Анотація:
碩士
玄奘大學
資訊管理學系碩士班
98
Autonomous surveillance systems are widely used as an important tool for security control in public areas. Among the numerous targets of the autonomous surveillance system, license plate recognition can help to identify the terrorist in cars. Uneven lighting conditions happen all the time in an autonomous surveillance system. As a result, traditional license plate detection can’t achieve the goal. In this research, we propose a novel method for license plate localization. The features of a license plate is generated in preprocess through the morphology. Then, apply the AdaBoost algorithm to select some weak classifiers from the weak classifier space to construct a strong classifier. Experimental results show that the proposed method can efficiently detect license plates under different illuminations.
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6

Tseng, Wei-Chung, and 曾瑋中. "License Plate Detection System of Low-resolution image." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/67347438421408621209.

Повний текст джерела
Анотація:
碩士
國立雲林科技大學
電機工程系碩士班
92
The amelioration of social order in our country caused by gun-fire event, extortion of car and human hostage, and robberies has greatly influence the society. This crisis has made security check and video surveillance become more important. Currently, the installation of video surveillance cameras is getting popular on the streets of many counties and cities. The recording of the passing vehicles and their license plate in these videos have successfully help the police solving many cases including hit-and-run, extortion of human hostage, etc.. However, search the video to extract and record the license plate (LP) of the suspected vehicles needs a lot of effort and time. This tedious work sometimes decreases the willingness and efficiency of the police in pursuing the criminals which places a great threat to our society. It''s obvious that the police needs an automatic system to extract the license plate from the video for them. In view of the above demand, we propose utilizing the techniques of image processing and develop an automatic LP detection system to suit the police department''s urgent need. Although most of current video cameras are equipped with a resolution of 640x480, to save the storage space, videos are usually taken at low resolution (320x240) and saved at high compression ratio. This available low resolution and poor quality data not only distinguish the research in this thesis from that of the other LP detection system, it also puts a great challenge to our design. In this thesis, we deal with both daytime and night-time video. On the basis of local variance of the image and techniques of morphology (e.g. bottom-hat, top-hat), method which detect location of the license plate was successfully developed. Videos of 12-hour daytime and 2-hour night-time taken by police department from several street scenes are used to evaluate the system performance. The results show that the LP detection rates are 93.74% and 78.57% for the daytime and night-time, respectively. Their corresponding false alarm rates are 1.17% and 15.58%.
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7

Kao, Sho-tsung, and 高碩聰. "An autonomous license plate detection and recognition system." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/72090163125968579734.

Повний текст джерела
Анотація:
碩士
國立臺南大學
數位學習科技學系碩士班
96
The paper proposed an autonomous license plate detection and recognition system with computer vision. The system consists of four subsystems: car detection subsystem, plate extraction subsystem, character division subsystem and character recognition subsystem. Car detection subsystem uses MMADR and NDDR of dynamic image to find the location of the cars on the screen. Plate extraction subsystem uses the characteristics of the plates and algorithm used to search plates to extract plate; character division subsystem combines Tophat, Labeling and LRE to automatically divide. As to character recognition subsystem, after comparing identification effects of SVM and BPNN, we choose BPNN as the recognizer. Experiment outcome proves that our system can effectively detect cars and recognize the plates under different lights.
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8

Yao, Chou-Yang, and 姚州陽. "License Plate Detection System Implementation by C Language." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/09082221285429681040.

Повний текст джерела
Анотація:
碩士
中華技術學院
電子工程研究所碩士班
98
The purpose of this research was to implement License Plate Detection System by C Language, in which MathWork MATLAB 7.6 and Microsoft Visual Studio 2005 were main development tools. Mex-function was implemented in C Language since it is essentially famous for efficiency, structure, portability and good readability. This study tries to take advantage of MATLAB API to dynamically link MEX library in run-time. The goal is turning script architecture (M-files) into Mex-functions. Programming process adopted a progressive approach to retain the benefits of MATLAB workspace, which means MATLAB built-in instructions are gradually replaced step by step. The method is able to easily verification and evaluation the performance by MATLAB’s friendly user interface. Photos taken from the digital camera provide the input images to the system in which environmental conditions could be day or night without external light source. Default accepted resolution is 1024x768 pixels. Experimental results showed that as long as the pixel numbers of license plate close to assumed 60x220, the characters could be recognized successfully. With appropriate tunned parameters, it can support more image sizes and variety of different plate pixels. The final system output is an image that re-combinated by the prefabricated digit/alphabet pictures according to the characters identification result from original images passed through processes of the license plate location, template matching and some pre-treatment.
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9

Hsu, Ren-Wei, and 許仁瑋. "A Real-Time System of Multiple License Plate Detection." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/22040438312555513397.

Повний текст джерела
Анотація:
碩士
國立中興大學
電機工程學系所
102
License Plate Recognition (LPR) has recently played a critical role in applications of Intelligent Transportation System (ITS), such as parking spaces management, records of traffic violations, and searches for stolen cars. However, although research on license plate recognition has been undertaken over many years, in practical applications, the accuracy of detection and identification can decrease because of the complex backgrounds and light changes in capture image. Therefore, recognition systems should be limited to specific environmental settings to obtain favorable results. In summary, interference caused by the environment is the main factor that limits the development of LPR. LPR and License Plate Detection (LPD) comprise two main steps, character segmentation and character identification. The license plate zone is detected prior to the two steps, and is the key to successful recognition of the license plate zone. Most LPR studies have investigated static and low-resolution images. Conversely, the present study involved using 720P high-definition video and a PCIe (PCI Express) image capture card to detect license plates. Subsequently, to achieve real-time LPR, Haar Wavelet Transform was applied in extracting an HL image, reducing the algorithm calculation time. Chapter 1 provides a review of relevant literature, and introduces the system architecture developed in this study. Chapter 2 introduces related techniques, including color space transform, motion detection, image sharpening, histogram equalization, Haar wavelet transform, edge detection, binarization, and connected component labeling. Chapter 3 describes the algorithm used for LPD in detail. First, the motion object is detected. The license plate features are then intensified, and adaptive thresholding is conducted to binarize the image. The edge is then connected, and connected component labeling is used to label each block. Thus, the license plate location is obtained. Chapter 4 introduces the experimental equipment and software interface used. In addition the experimental results obtained using the aforementioned algorithm in this study are described. In the final chapter, a conclusion is presented and possible methods of the amendments are suggested in accordance with the experimental results.
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10

ANAND, SHUBHAM. "DESIGN AND EVALUATE LICENSE PLATE DETECTION SYSTEM BASED ON SEGMENTATION." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18364.

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Анотація:
A continual upsurge in the volume of vehicles has been noticed over the past few decades with the increase in population all over the world. Therefore, tracking of vehicles depending upon the number plates is crucial to guarantee the control of vehicular traffic in competent manner. The vehicles can be detected on the basis of their tags with the help of a new image processing-based technology referred as ANPR (Automatic Number Plate Recognition) the expertise is ahead of time ubiquity to ensure security and traffic management. This system makes use of computer vision approach for extracting information regarding the abnormal state from a digital image using a computer. Almost all number plate localization algorithms combine many processes that result in a long computational time. Most of the image details are lost or image quality gets degraded as a result of complex, noisy content in images. The non-consistency of processes cause degradation which in turn affects the image quality. The car number plate detection has many stages. In this research work, technique of voting classifier is used for detecting the number plates of cars. For the purpose of voting classification, we have used a unique combination of classifiers. The voting classification proposed in this research work for the number plate detection is the combination of SVM and random forest classifier. The MATLAB and/or GNU Octave has been used for the evaluation of the proposed model. The efficiency of new algorithmic approach is examined with respect to accuracy, precision and recall. The proposed algorithm gives accuracy up to 95 percent for the car number plate detection. Similar, observation with the Precision and the Recall that comes out to be 95.81 percent and 95.45 percent respectively.
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Книги з теми "LICENSE PLATE DETECTION SYSTEM"

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Auditor, Colorado Office of State. License plate management system, performance audit. [Denver, Colo: Colorado State Auditor, 2002.

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2

New, Jersey Legislature Senate Committee on Law Public Safety and Defense. Public hearing before Senate Law, Public Safety, and Defense Committee: Continuation of February 25, 1991 hearing to receive testimony from individuals and organizations on the recently announced plans to change the standards and procedures for the motor vehicle inspection system administered by the Division of Motor Vehicles (see previous transcript dated 2/25/91) : March 11, 1991, Room 407, State House Annex, Trenton, New Jersey. Trenton, N.J: The Committee, 1991.

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3

Sparrow, Malcolm K. License to steal: Why fraud plagues America's health care system. Boulder, Colo: Westview Press, 1996.

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4

Chiles, Lawton, 1930-1998, writer of foreword, ed. License to steal: Why fraud bleeds America's health care system. New York, NY: Routledge, 2018.

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License to Steal: How Fraud Bleeds America's Health Care System. Westview Press, 2000.

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6

Sparrow, Malcolm K. License to Steal: How Fraud Bleeds America's Health Care System, Updated Edition. Taylor & Francis Group, 2019.

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7

Sparrow, Malcolm K. License to Steal: How Fraud Bleeds America's Health Care System, Updated Edition. Taylor & Francis Group, 2019.

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Частини книг з теми "LICENSE PLATE DETECTION SYSTEM"

1

Aruna, V. S., and S. Ravi. "License Plate Detection and Recognition—A Review." In Advances in Intelligent Systems and Computing, 777–92. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3608-3_54.

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Bhatta, Shiwani, Harshit Srivastava, Santos Kumar Das, and Poonam Singh. "License Plate Detection for Smart Parking Management." In Advances in Power Systems and Energy Management, 71–79. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7504-4_8.

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3

Islam, Tariqul, and Dm Mehedi Hasan Abid. "Automatic Vehicle Bangla License Plate Detection and Recognition." In Algorithms for Intelligent Systems, 523–34. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3311-0_44.

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Rahman, Md J., S. S. Beauchemin, and M. A. Bauer. "License Plate Detection and Recognition: An Empirical Study." In Advances in Intelligent Systems and Computing, 339–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17795-9_24.

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Nie, Wenzhen, Pengyu Liu, and Kebin Jia. "License Plate Occlusion Detection Based on Character Jump." In Advances in Intelligent Systems and Computing, 233–42. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5841-8_25.

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Mozumder, Madhurya, Souharda Biswas, L. Vijayakumari, R. Naresh, C. N. S. Vinoth Kumar, and G. Karthika. "An Hybrid Edge Algorithm for Vehicle License Plate Detection." In Intelligent Sustainable Systems, 209–19. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1726-6_16.

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Kim, Jong-Bae. "MSER and SVM-Based Vehicle License Plate Detection and Recognition System." In Convergence and Hybrid Information Technology, 529–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32692-9_66.

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8

Prates, Raphael C., Guillermo Cámara-Chávez, William Robson Schwartz, and David Menotti. "An Adaptive Vehicle License Plate Detection at Higher Matching Degree." In Advanced Information Systems Engineering, 454–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_56.

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9

Salau, Ayodeji Olalekan. "An Effective Graph-Cut Segmentation Approach for License Plate Detection." In Advances in Intelligent Systems and Computing, 19–32. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2740-1_2.

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Liu, Yinan, Yangzhou Chen, Jianqiang Ren, and Le Xin. "Calculating Vehicle-to-Vehicle Distance Based on License Plate Detection." In Advances in Intelligent Systems and Computing, 485–97. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38789-5_57.

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Тези доповідей конференцій з теми "LICENSE PLATE DETECTION SYSTEM"

1

Aung, Khin Pa Pa, Khin Htar Nwe, and Atsuo Yoshitaka. "Automatic License Plate Detection System for Myanmar Vehicle License Plates." In 2019 International Conference on Advanced Information Technologies (ICAIT). IEEE, 2019. http://dx.doi.org/10.1109/aitc.2019.8921286.

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2

Anoual, Hinde, Sanaa El Fkihi, Abdellilah Jilbab, and Driss Aboutajdine. "Vehicle license plate detection in images." In 2011 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2011. http://dx.doi.org/10.1109/icmcs.2011.5945680.

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Musil, Petr, Roman Juránek, and Pavel Zemčík. "Unconstrained License Plate Detection in Hardware." In 7th International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010174000130021.

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Musil, Petr, Roman Juránek, and Pavel Zemčík. "Unconstrained License Plate Detection in Hardware." In 7th International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010174000002932.

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5

Liao, Yu. "Research on Edge Detection in License Plate Recognition." In 2nd International Conference on Computer Application and System Modeling. Paris, France: Atlantis Press, 2012. http://dx.doi.org/10.2991/iccasm.2012.290.

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6

Sambhavi, D. V. Shrija, Shruthi Koushik, and Rameeza Fathima. "Indian License Plate and Vehicle Type Recognition." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-24rciz.

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In light of the growing number of vehicles, automated license plate recognition (ALPR) systems are much needed. The ALPR system is a widely used technology for various vehicle management processes such as law enforcement, surveillance, toll booth operations, parking lots, etc. We propose a license plate recognition system, where a neural network concept is applied. This system includes image pre-processing which helps to quickly and easily locate, segment and recognize the license plate characters, so image pre-processing is one of the important factors that affect total system performance. As we are performing the character segmentation of the license plate, the accuracy of the character recognition increases. In India, license plates are not only different in shape and size but also have different colours according to the registration or license number in India. There are 8 types of license plates in total issued by the RTO. In this effort, we identify the type of license plate by detecting the colour of the license plate. Thus vehicle registration types are recognized from the colour of the license plate detected.
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Alhaj Mustafa, Haneen, Sara Hassanin, and Musa Al-Yaman. "Automatic Jordanian License Plate Recognition System Using Multistage Detection." In 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2018. http://dx.doi.org/10.1109/ssd.2018.8570408.

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8

Nguyen, Chi Toan, Thanh Binh Nguyen, and Sun-Tae Chung. "Reliable detection and skew correction method of license plate for PTZ camera-based license plate recognition system." In 2015 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2015. http://dx.doi.org/10.1109/ictc.2015.7354726.

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9

Deb, Kaushik, and Kang-Hyun Jo. "HSI color based vehicle license plate detection." In 2008 International Conference on Control, Automation and Systems (ICCAS). IEEE, 2008. http://dx.doi.org/10.1109/iccas.2008.4694589.

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Bakar, Norazhar Abu, Mohamad Riduwan Md Nawawi, Abdul Rahim Abdullah, Aminurrashid Noordin, Zainolizam Musa, and Oon Cee Xian. "Malaysian vehicle license plate recognition using double edge detection." In 2012 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2012. http://dx.doi.org/10.1109/iccsce.2012.6487182.

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Звіти організацій з теми "LICENSE PLATE DETECTION SYSTEM"

1

Thomas, Michael J. Combining Facial Recognition, Automatic License Plate Readers and Closed Circuit Television to Create an Interstate Identification System for Wanted Subjects. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1009302.

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Johnson, Anna K., Kenneth J. Stalder, Robert F. Fitzgerald, Steven J. Hoff, Gang Sun, Locke A. Karriker, and Johann Coetzee. Induction of a Transient Chemically Induced Lameness in the Sow. Detection Using a Prototype Embedded Micro-computerbased Force Plate System. Ames (Iowa): Iowa State University, January 2011. http://dx.doi.org/10.31274/ans_air-180814-277.

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3

Ginzel. L51748 Detection of Stress Corrosion Induced Toe Cracks-Advancement of the Developed Technique. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), March 1996. http://dx.doi.org/10.55274/r0010659.

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In the past few years an ongoing problem has existed with stress corrosion cracking (SCC) in pipelines around the world. Several member companies of the Pipeline Research Council International, Inc. have experienced multiple incidents as a result of ERW defects and SCC. TCPL is running a series of hydrostatic tests and trial digs to identify the most severely affected areas. These excavations and failure studies have ascertained that most of the SCC causing failure has been on the outside diameter of long seam welded pipe at the edge of the weld. Defects at that location are known as "Toe-Cracks" Ginzel has developed an ultrasonic inspection technique that will detect both SCC colonies and toe cracks in long seam pipe. The main design objective for this research project was the selection and placement of ultrasonic transducers to combine weld, plate thickness and lamination inspection, along with SCC detection and sizing. Examination of sample pipe sections to demonstrate its success is reported. The primary stages for this research project are: �Assemble test equipment Establish test procedure System trials and data collection Evaluation of system performance and collected data Correlation of test data - Results
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