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Detekce typu a bodového ohodnocení kartiček ve hře Hobiti
Hlinský, Martin ; Kohút, Jan (referee) ; Vaško, Marek (advisor)
This thesis aims to create a card detector that can train a model that can detect the score of a card and its type using the synthetic generation of the dataset. The YOLOv8 model is used for training. The first step is to take pictures of the cards, which then go through a pre-processing stage so they do not contain background and are aligned. These pre-processed card images are combined with photos from other datasets in a generator that randomly translates, rotates, and otherwise simulates photos of possible card placements. This generator’s output is roughly 50 000 annotated images in the case of the Hobiti game, but different dataset sizes and pre-trained weights are compared in the experiments. The latest generation of trained detectors was validated on a real dataset for unbiased testing, and the most accurate model trained on purely synthetic datasets achieved precision up to 81.5 % according to the 50 metric. It is then possible to implement, for example, a point counter on the final detector, a prototype of which is also described in this paper.
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Detekce karet při turnajích v pokru
Kovalets, Vladyslav ; Šilling, Petr (referee) ; Vaško, Marek (advisor)
This bachelor's thesis focuses on the development of an advanced system for automatic recognition and registration of playing cards from video recordings of poker games. The technology of convolutional neural networks, specifically the YOLO network, was chosen as the basic tool. It enables effective identification of cards on the table and in the hands of players even under challenging conditions. The work involved creating an extensive dataset for training and testing the card detector, which achieved a recognition accuracy of 98.7%. An algorithm was designed to minimize detector errors and improve the overall accuracy of the system. The results of the study suggest that the developed system has potential for use in practice.
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Automatická kontrola dopravního značení
Čechmánek, Roman ; Klíma, Ondřej (referee) ; Musil, Petr (advisor)
The aim of this work is to create a cost-effective tool capable that would be able to automate the process of traffic sign control. This includes working with records of drives on land communications, created using inexpensive recording devices such as GoPro action cameras or certain dashcams. The control is based on the system localized traffic signs and historical traffic sign mapping data. The result of the work is a system whose input consists of drive records and historical data, and whose output is two files containing information about the inspection results. The first of these is a GEOJSON file, suitable for further processing of the collected data, and an HTML file that provides a simple user interface visualizing the inspection results on an interactive web map.
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Automated Metadata Extraction From Document Images
Křivánek, Jakub ; Vaško, Marek (referee) ; Kohút, Jan (advisor)
This Bachelor thesis addresses the problem of extracting structured data from scans of documents from Czech libraries. The aim of the thesis is to simplify the time-consuming manual process for librarians. I focused on creating datasets from documents of Czech libraries and on detecting metadata on these datasets. I created one dataset for books and another for periodicals. Detection was performed by classifying lines read from the documents. This utilized a fully connected neural network and a network employing a Transformer Encoder. The second method of metadata detection is based on object detection in document scans using the YOLOv8 model. Detection using the fully connected neural network achieves an F1 score of 0.83 on the book dataset and 0.78 on the periodicals dataset. The Transformer Encoder network achieves F1 scores of 0.84 on the book dataset and 0.59 on the periodicals dataset. The YOLO model achieves an F1 score of 0.86 (confidence at 0.549) on the book dataset and 0.7 (confidence at 0.336) on the periodicals dataset.
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Detection of Material Surface Damage Based on a Photograph
Marek, Radek ; Sakin, Martin (referee) ; Dyk, Tomáš (advisor)
This work focuses on the use of various types of neural networks for detecting surface damage of materials from photographs and evaluates their effectiveness. Identifying different types of damage, such as cracks, scratches, and other defects, is essential for assessing the condition of materials and may indicate the need for further maintenance or repairs. The use of advanced neural networks allows for more precise detection and classification of damage, which is crucial for applications in areas such as construction, the automotive industry, and aerospace engineering, where rapid and reliable diagnostics of material defects are critical. Integrating these technologies into regular inspection processes can significantly improve accident prevention and extend the lifespan of structural components. The work also discusses the possibilities for improvement and adaptation of algorithms to specific materials and types of damage. Thus, this work demonstrates how advanced machine learning technologies can significantly contribute to more effective and reliable material condition monitoring, opening paths for future innovations in maintenance and safety.
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Tracking people based on their clothing in multi-camera systems
Sivak, Mykyta ; Přinosil, Jiří (referee) ; Číka, Petr (advisor)
This bachelor thesis focuses on the development and implementation of an algorithm for tracking individuals in multi-camera systems based on clothing pattern analysis. The aim was to design a system capable of tracking an individual in various positions and frames, using the Region of Interest (RoI) technique. The study begins with a comprehensive review of the existing literature on object tracking in video sequences, with a special focus on RoI tracking techniques. During the research, a new algorithm was developed and implemented that utilizes clothing patterns as the primary identification element for tracking and re-identifying individuals across different camera shots. The algorithm was experimentally validated on datasets containing video sequences from various environments, allowing for a detailed analysis of its effectiveness and reliability. The experimental results demonstrate that the proposed system achieves significant accuracy and efficiency compared to traditional methods and is particularly effective in challenging situations where other methods fail. The thesis concludes with an evaluation of the conducted experiments along with recommendations for future extensions and improvements of the system. Potential challenges and ethical aspects, including issues of privacy and personal data processing, are also discussed.
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Detection of a Semi-Structured Semi-Finished Product from a Defined Area Using Artificial Intelligence Methods
Zmrzlý, Jan ; Škrabánek, Pavel (referee) ; Juříček, Martin (advisor)
This thesis addresses the issue of machine vision in the context of Industry 4.0, with an emphasis on the detection of semi-structured objects from surfaces. The first part of the thesis discusses the theoretical aspects of the task, including selected machine vision algorithms and the use of neural networks in this area. Furthermore, a survey of the available methods for solving this problem is conducted, as well as the current state of the art of the EDUset ONE robotic cell with respect to machine vision. Based on the analysis, a hardware solution in the form of camera, lighting and other components is proposed. Subsequently, the design and implementation of different methods for detecting multiple types of objects is carried out, with emphasis on modularity, efficiency and accuracy. Finally, the work compares these methods and verifies their functionality in interaction with a real robotic cell.
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Device for the traffic situation evaluating
Gábel, Matej ; Honec, Peter (referee) ; Janáková, Ilona (advisor)
The bachelor's thesis deals with the implementation of a device for evaluating the traffic situation, specifically by traffic signs detection. In this work, I tried different methods of traffic sign recognition, where the resulting implementation on hardware is done using convolutional neural networks. More precisely, it is the YOLOv5 architecture, which is suitable for recognizing traffic signs in real time.
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Intracranial hemorrhage localization in axial slices of head CT images
Kopečný, Kryštof ; Chmelík, Jiří (referee) ; Nemček, Jakub (advisor)
This thesis is focused on detection of intracranial hemorrhage in CT images using both one-stage and two-stage object detectors based on convolutional neural networks. The fundamentals of intracranial hemorrhage pathology and CT imaging as well as essential insight into computer vision and object detection are listed in this work. The knowledge of these fields of studies is a starting point for the implemenation of hemorrhage detector. The use of open-source CT image datasets is also discussed. The final part of this thesis is a model evaluation on a test dataset and results examination.
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Tracking People in Video Captured from a Drone
Lukáč, Jakub ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Práca rieši možnosť zaznamenávať pozíciu osôb v zázname z kamery drona a určovať ich polohu. Absolútna pozícia sledovanej osoby je odvodená vzhľadom k pozícii kamery, teda vzhľadom k umiestneniu drona vybaveného príslušnými senzormi. Zistené dáta sú po ich spracovaní vykreslené ako príslušné cesty v grafe. Práca si ďalej dáva za cieľ využiť dostupné riešenia čiastkových problémov: detekcia osôb v obraze, identifikácia jednotlivých osôb v čase, určenie vzdialenosti objektu od kamery, spracovanie potrebných senzorových dát. Následne využiť preskúmané metódy a navrhnúť riešenie, ktoré bude v reálnom čase pracovať na uvedenom probléme. Implementačná časť spočíva vo využití akcelerátoru Intel NCS v spojení s Raspberry Pi priamo ako súčasť drona. Výsledný systém je schopný generovať výstup o polohe detekovaných osôb v zábere kamery a príslušne ho prezentovať.
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