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Lidar systems testing considerations for field use
Picmausová, Helena ; Farlík, Jan ; Eichhorn, Marc ; Kieleck, Christelle
The aim of this paper is to offer a perspective on testing a commercially available lidar system in order to determine its suitability for various practical tasks including mapping, object recognition, and the potential in its coupling with another sensor, in this case a camera. Several mapping missions were carried out over the course of the experiment, with both the lidar and the camera mounted on an Unmanned Aircraft System. Aside from mapping ordinary objects like trees, vehicles, people, and ground elevation, a standardized test target was designed for the purposes of the experiment, and placed in detection area. Influence of external factors on system performance was evaluated, e.g. atmospheric conditions and material properties of scanned surfaces, especially their reflectivity. Post processing of obtained data was carried out, demonstrating the potential of overlaying multiple sensor data for easier object recognition, and an optimal use case for the system is suggested.
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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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Machine vision implementation in the UVSSR PORTABLE CELL production system
Gómez Rojas, José Luis ; Kroupa, Jiří (referee) ; Bražina, Jakub (advisor)
This thesis investigates the integration of computer vision into Industry 4.0, utilizing the UVSSR CELL at Brno University of Technology. Focused on enhancing virtual commissioning, it introduces three innovative vision techniques linked via an OPC server to an IoT gateway. Object recognition, hand gesture control, and facial recognition are employed, improving robotic arm operations and security protocols. This integration resulted in high accuracy trained model for object detection with mAP50-90 close to 0.9, and control precision of the technologies and the virtual environment, contributing significantly to smart industry automation and setting a call for future work on top of it. The thesis covers methodology, technological implementation, and prospects for advanced, efficient machine vision systems within industry 4.0.
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Road Transport Analysis Using Neural Networks
Žárský, Daniel ; Musil, Petr (referee) ; Smrž, Pavel (advisor)
Cílem této bakalářské práce je zjednodušit analýzu silničního provozu, která využívá kamerové záznamy, a to poskutnutím prostředku pro automatickou annotaci scény. Práce popisuje obecné technické pricipy využité v kamerovém systému monitorujícím dopravu a navrhuje postup zpracování dat, získaných metodami počítačového vidění, s cílem automatizovaného nasazení systému. Následné zpracování dat využívá klastrovacích algoritmů pro identifikaci a lokalizaci hlavních směrů pohybu účastníků dopravnícho provozu. Na základě těchto výsledků je scéna automaticky annotována. Anotace scény je použitelná jako základ pozdější detekce anomálií v dopravě v reálném čase.
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Object Detection on the i.MX RT Microcontroller
Kravchuk, Marina ; Rozman, Jaroslav (referee) ; Janoušek, Vladimír (advisor)
This work focuses on the use of machine learning, particularly convolutional neural networks, in industrial applications. The course of work involves investigating the implementation of these networks directly on embedded devices, specifically NXP i.MX RT microcontrollers. During the course of the study, materials related to the training and use of neural networks and their optimization for deployment on low power devices were reviewed. Several neural network models were trained and tested, the best of which was used in the final version of the application. The application itself is divided into two parts: one part is written in C/C++ in the MCUXpresso IDE, where the main functionality of the program is implemented, while the other part of the work, i.e. the creation of a graphical user interface to control the program, is done in Python. The result is a functional application for the MIMXRT1170-EVK microcontroller that is able to detect and recognize small colored objects of certain shapes from a predefined data set.
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Automatická vizuální podpora pro Q-řazení
Kán, Dávid ; Hradiš, Michal (referee) ; Vaško, Marek (advisor)
This bachelor thesis deals with the integration of Q-sorting and computer vision methods for object detection. The goal of the work is to create a program that, with the help of~visual support, will facilitate the process and at the same time prevent errors in Q-sorting. Furthermore, the work deals with the creation of~a suitable data set for training the model and for experiments, which takes into account the way the cards are laid out and the~environment. The implemented program takes the form of a console application and is written using the Python programming language. The program uses YOLOv8 to detect objects and uses Pero OCR to retrieve text from cards. Using the created test set, experiments were performed on the trained model and the program was tested.
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Mobilní aplikace pro podporu trénování silových sportů
Košina, Simon ; Vaško, Marek (referee) ; Juránek, Roman (advisor)
The aim of this work is to create a mobile application for Android devices that provides athletes with real-time feedback during strength training in the form of velocity metrics for individual repetitions within a set of a certain exercise. Velocity based training is becoming increasingly popular both in practical applications and in research, where it has been demonstrated that these objective metrics can be used to estimate the intensity of a given set. The resulting application utilizes machine learning methods to detect weights plates loaded on a barbell in frames coming from the mobile device's camera and tracking their movement trajectory. Known size of the weight plates is used to calibrate the travelled distance. The algorithm operates in real-time, providing users with feedback during exercise sessions in the form of an auditory signal when a predefined threshold of selected velocity metric is reached.
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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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Segmentation of logical units in text
Kostelník, Martin ; Kišš, Martin (referee) ; Beneš, Karel (advisor)
Cílem projektu bylo vytvořit systém pro automatickou segmentaci textu do logických celků. Práce staví na systému PERO-OCR a cílí na zlepšení zpracovávání českých historických dokumentů a jejich vyhledávačů používaných knihovníky a vědci. Práce zahrnovala vytvoření a anotace vlastní datové sady složené celkem z 4044 stránek z knih, slovníků a novin. K problému segmentaci textu je přistoupeno inovativních přístupem, kdy je brán jako shlukovací problém jednotlivých řádků textu. Metoda je dvoufázová: nejprve probíhá detekce regionů textu pomocí modelu YOLOv8 a následuje jejich spojení grafovou neuronovou sítí. Vyhodnocení je provedeno pomocí shlukovací metriky V-measure a na testovacím datasetu dosahuje hodnot 77.93 % pro knihy, 95.79 % pro slovníky a 90.23 % pro noviny.
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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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