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Detection and Recognition of License Plates
Tykva, Jiří ; Zemčík, Pavel (referee) ; Juránek, Roman (advisor)
Cílem této bakalářské práce je návrh, implementace a testování systému, který v reálném čase pomocí neuronových sítí bude detekovat a rozpoznávat registrační značky vozidel. Nasbíraná data budou ukládána do databáze. Architektura systému je rozdělena do tří hlavních částí. První část řeší detekci registrační značky v obraze pomocí TensorFlow Object Detection API. Detektor dosahuje přesnosti 98.15 % AP při rychlosti kolem 14 fps. Druhá část se zabývá sledováním značek ve videu pomocí algoritmu SORT. Třetí část systému se věnuje holistickému rozpoznávání textu registrační značky a dosahuje až 0.6% chybovosti při rozpoznávání jednotlivých znaků a 2% chybovosti při rozpoznávání celého textu. Výsledný systém lze použít například pro policejní oddělení za účelem sledování kradených vozidel či automatického vybírání dálničních poplatků.
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Car Licence Plate Detection and Recognition
Kovaříček, Roman ; Procházka, Boris (referee) ; Váňa, Jan (advisor)
This bachelor thesis deals with finding the license plates in the image and pattern recognition. Work describes short history of the state license plates. It deals with also the current state license plates and their problems. It analyzes the process of image segmentation and follow evaluation of selected areas. A part of the work is design and implementation of algorithms that solve candidate search areas or characters. The final step is the recognition of individual characters and display the user with details of the result.
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Holistic License Plate Recognition Based on Convolution Neural Networks
Le, Hoang Anh ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
Main goal of this work was to create a holistic license plate reader, with an emphasis on achieving the highest possible accuracy on low quality images. Combination of convolutional and recurrent neural networks was designed and implemented, with usage of LSTM and CTC, where the inputs are cut-outs from the entire license plate. Competitive networks were also implemented to compare results. Networks were compared on a total of 4 datasets and the results were, that my design has achieved the best results with a recognition accuracy of 97.6%.
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Image-Based Licence Plate Recognition
Vacek, Michal ; Hradiš, Michal (referee) ; Beran, Vítězslav (advisor)
In first part thesis contains known methods of license plate detection. Preprocessing-based methods, AdaBoost-based methods and extremal region detection methods are described.Finally, there is a described and implemented own access using local detectors to creating visual vocabulary, which is used to plate recognition. All measurements are summarized on the end.
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License Plate Detection and Recognition
Řepka, Michal ; Sochor, Jakub (referee) ; Herout, Adam (advisor)
This paper addresses the problem of object detection and recognition from still images using methods of computer vision. The objects of detection are czech license plates and the goal of this paper was to create an automatic license plate anotation tool. Suggested solution uses edge detection and machine learned cascading classifiers. Created application was then tested on dataset taken by the author.
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License Plate Detection and Localization
Šlosár, Peter ; Beran, Vítězslav (referee) ; Herout, Adam (advisor)
This bachelor's thesis deals with the detection and localization of vehicle registration plates. Theoretical part discusses properties and appearance of Czech and Slovak license plates and also methods presently used for detection and localization of license plates. Main part of the thesis consists of design and implementation of new detection and localization method using corner detector, clustering and cascade classification. Final part describes testing of this system using dataset and evaluates its success rate.
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Obtaining and Processing of a Set of Vehicle License Plates
Kvapilová, Aneta ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
This master thesis focuses on creating and processing a dataset, which contains semi-automatically processed images of vehicles licence plates. The main goal is to create videos and a set of tools, which are able to transform input videos into a dataset used for traffic monitoring neural networks. Used programming language is Python, graphical library OpenCV and framework PyTorch for implementation of neural network.
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License Plate Detection and Recognition from Still Image
Janíček, Kryštof ; Sochor, Jakub (referee) ; Špaňhel, Jakub (advisor)
This thesis describes the design and implementation of system for detection and recognition of license plate. This system is divided into three parts which are license plate detection, character segmentation and optical character recognition. License plate detection is done by cascade classifier that achieves hit rate of 95.5% and precision rate of 95.9%. Character segmentation is based on contour finding that achieves hit rate of 93.3% and precision rate of 96.5%. Optical character recognition is done by neural network and achieves hit rate of 98.4% for individual characters. The whole system is able to detect and recognize up to 81.5% of license plates from the test data set.
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Car Licence Plate Anonymization
Skřivánková, Barbora ; Zachariášová, Marcela (referee) ; Drahošová, Michaela (advisor)
While browsing an online map server, continuous photographs of certain places can be browsed as well. When the map service takes pictures of a public space, there are some personal data captured as well (i.e. faces, car licence plates). The goal of this thesis is the design of automated car licence plates anonymization system, optimized for the Panorama service provided by the Seznam.cz a.s. corporation. In this thesis, the process of car licence plate anonymization is divided into two parts: the first one solves a detection of cars and the second solves a car licence plate localization in the selected image. The car detection is based on the deep neural network approach, the car licence plate localization is solved by using a fully connected neural network performing a regression task. The goal of this thesis is to get over the disadvantages of commercial solution used nowadays. These are false posititive results and high computational complexity. Results of this thesis are not as good as expected. The reason could be a dataset provided by Seznam.cz a.s. corporation, which seemed to be robust enough in the beginning, but in the end it showed up to be not suffice enough to train the neural network.
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