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Assembling and creating tasks for an interactive robotic head
Szabó, Michal ; Formánek, Martin (referee) ; Bastl, Michal (advisor)
This bachelor’s thesis deals with the creation of the model of an interactive robotic head. The work itself is divided into two parts, theoretical and practical. The theoretical part is devoted to an overview of the types of robotic heads, a brief description of the available tools for recognizing objects in the image and tools for recognizing spoken speech in real time. The practical part is focused on the tools used in programming of this model, the electronics used and the resulting model of the robotic head. Finally, there are described programmed functions enabling various ways of the interaction with humans. The work includes function scripts programmed in Python.
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Visual Localization of Chess Pieces
Hampl, Tomáš ; Špaňhel, Jakub (referee) ; Hradiš, Michal (advisor)
The main goal of this thesis was to analyze state of the chess game and to locate chess pieces on the chessboard. Chessboard recognition is based on locating lines in image using Hough transform and PClines. The figures were detected by models of convolutional neural networks - YOLOv3, YOLOv4 and YOLOv4 tiny. Evaluation was perfomed on our data set. Chessboard detection achieves accuracy of 97%, complete localization of the state reaches 74,5% and piece localization 96%.
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Detection of Boxes in Image
Soroka, Matej ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The aim of this work is to experiment and evaluate different approaches of computer vision with the aim of automatic detection of boxes-blocks in the image, for this purpose, approaches based on neural networks were used in the solution. Experiments were performed with classification using our own data set, classification using our own convolutional neural network, detection using a window, YOLO detector and in the last part a proposal for improvement using U-net and MirrorNet networks.
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Deep learning model for visual detection and classification general object from industry
Dočkal, Radim ; Honec, Peter (referee) ; Kratochvíla, Lukáš (advisor)
The goal of this bachelor’s thesis is to programme deep learning model for visual detection and classification of general object from industry. The paper is divided into five chapters. First chapter deals with research of the most used architectures of this type. The second chapter deals with choosing the best fitting architecture for usage in industry. In the third chapter is desribed the procedure of creating your own dataset. The fourth chapter then describes the implementation process itself, so that each sub-part of the architecture was sufficiently described and the results are described in the fifht chapter. The summary and recommended procedures for potential implementation in real environment can be found in the conclusion of this paper.
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Advanced analysis of moving objects in the image
Medynskyi, Ivan ; Sikora, Pavel (referee) ; Kiac, Martin (advisor)
This work focuses on image processing using the OpenCV library and detecting moving objects in video using convolutional neural networks. The resulting application can detect moving objects in video or in real time and includes a user interface that allows the user to easily control the application. Part of this application is the YOLO convolutional model, which is designed to detect moving objects.
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Detection and classification of flying objects
Jurečka, Tomáš ; Richter, Miloslav (referee) ; Janáková, Ilona (advisor)
The thesis deals with the detection and classification of flying objects. The work can be divided into three parts. The first part describes the creation of dataset of flying objects. The reverse image search is used to create the dataset. The next part is a research of algorithms for detection, tracking and classification. Subsequently, the individual algorithms are applied and evaluated. In the last part, the design of hardware components is performed.
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Not So Close That Breck
Smrekovský, Adam ; Jančík, Alexandr (referee) ; Šrámek, Jan (advisor)
How to stop working, and start catching dogs, and live alone on a hill and walk down only when you need to catch a dog. But it also stops entertaining you and you start thinking about village life. And so you run away from the end of the world to the village.
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Detection of Traffic Signs and Lights
Oškera, Jan ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
The thesis focuses on modern methods of traffic sign detection and traffic lights detection directly in traffic and with use of back analysis. The main subject is convolutional neural networks (CNN). The solution is using convolutional neural networks of YOLO type. The main goal of this thesis is to achieve the greatest possible optimization of speed and accuracy of models. Examines suitable datasets. A number of datasets are used for training and testing. These are composed of real and synthetic data sets. For training and testing, the data were preprocessed using the Yolo mark tool. The training of the model was carried out at a computer center belonging to the virtual organization MetaCentrum VO. Due to the quantifiable evaluation of the detector quality, a program was created statistically and graphically showing its success with use of ROC curve and evaluation protocol COCO. In this thesis I created a model that achieved a success average rate of up to 81 %. The thesis shows the best choice of threshold across versions, sizes and IoU. Extension for mobile phones in TensorFlow Lite and Flutter have also been created.
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System for People Detection and Localization Using Thermal Imaging Cameras
Charvát, Michal ; Kempter, Guido (referee) ; Drahanský, Martin (advisor)
V dnešním světě je neustále se zvyšující poptávka po spolehlivých automatizovaných mechanismech pro detekci a lokalizaci osob pro různé účely -- od analýzy pohybu návštěvníků v muzeích přes ovládání chytrých domovů až po hlídání nebezpečných oblastí, jimiž jsou například nástupiště vlakových stanic. Představujeme metodu detekce a lokalizace osob s pomocí nízkonákladových termálních kamer FLIR Lepton 3.5 a malých počítačů Raspberry Pi 3B+. Tento projekt, navazující na předchozí bakalářský projekt "Detekce lidí v místnosti za použití nízkonákladové termální kamery", nově podporuje modelování komplexních scén s polygonálními okraji a více termálními kamerami. V této práci představujeme vylepšenou knihovnu řízení a snímání pro kameru Lepton 3.5, novou techniku detekce lidí používající nejmodernější YOLO (You Only Look Once) detektor objektů v reálném čase, založený na hlubokých neuronových sítích, dále novou automaticky konfigurovatelnou termální jednotku, chráněnou schránkou z 3D tiskárny pro bezpečnou manipulaci, a v neposlední řadě také podrobný návod instalace detekčního systému do nového prostředí a další podpůrné nástroje a vylepšení. Výsledky nového systému demonstrujeme příkladem analýzy pohybu osob v Národním muzeu v Praze.
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Visual detection of small objects using available tools in MATLAB
Sladký, Jiří ; Dobossy, Barnabás (referee) ; Appel, Martin (advisor)
This thesis investigates possibilities of small object detection in pictures using YOLO method, a deep learning algorithm available in MATLAB. In the thesis, a detector was designed and trained to detect cows from top-down view. A tool was created, that performs detection using the proposed model even on high resolution images and counts the present objects. A generator of synthetic images was programmed, which helped with training the model. Various experiments were performed that found the limits of YOLO and validated contribution of the proposed improvements.
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