National Repository of Grey Literature 65 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
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.
Graffiti Tags Re-Identification
Pavlica, Jan ; Beran, Vítězslav (referee) ; Špaňhel, Jakub (advisor)
This thesis focuses on the possibility of using current methods in the field of computer vision to re-identify graffiti tags. The work examines the possibility of using convolutional neural networks to re-identify graffiti tags, which are the most common type of graffiti. The work experimented with various models of convolutional neural networks, the most suitable of which was MobileNet using the triplet loss function, which managed to achieve a mAP of 36.02%.
Counting Vehicles in Static Images
Zemánek, Ondřej ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
Tato práce se zaměřuje na problém počítání vozidel v statickém obraze bez znalosti geometrických vlastností scény. V rámci řešení bylo implementováno a natrénováno 5 architektur konvolučních neuronových sítí. Také byl pořízen rozsáhlý dataset s 19 310 snímky pořízených z 12pohledů a zachycujících 7 různých scén. Použité konvoluční sítě mapují vstupní vzorek na mapu hustoty vozidel, ze které lze získat jejich počet a lokalizaci v kontextu vstupního snímku. Hlavním přínosem této práce je porovnání a aplikace dosavadních nejlepších řešení pro počítání objektů v obraze. Většina z těchto architektur byla navržena pro počítání lidí v obraze, proto musely být uzpůsobeny pro potřeby počítání vozidel v statickém obraze. Natrénované modely jsou vyhodnoceny GAME metrikou na TRANCOS datasetu a na velkém spojeném datasetu. Dosažené výsledky všech modelů jsou následně popsány a porovnány.
Detection of Vehicles in Image and Video
Petráš, Adam ; Zemčík, Pavel (referee) ; Špaňhel, Jakub (advisor)
This bachelor thesis is focused on vehicle detection. The thesis deals with the method of vehicle detection using convolutional neural networks, their structures and models. All scripts were implemented using python programming language with Tensorflow Object Detection API interface. The first part of this thesis was devote to the structures of popular neural networks and models of detection neural networks. The next chapter deals with the most famous frameworks that are used for machine learning. Three neural network models were selected and trained on the COD20K dataset. The result of this thesis is statistics that discuss the efficiency and performance of each model on trained dataset and compare performance without displaying video on Nvidia RTX 2060, where the performace archieved by SSD MobileNet V2 network was 300FPS and Nvidia Tegra TX2 8GB, whose performace reached almost 44FPS.
Web-Based Image Annotation Tool
Dvořáček, David ; Kapinus, Michal (referee) ; Špaňhel, Jakub (advisor)
This work deals with the creation of web tool for image data annotation. The theoretical part specifies the application, its design and functionality. The practical part deals with the implementation of the web tool for image data annotation such as point, line, rectangle, polygon with focus on modularity and easy extensibility of the tool for various types of annotations and implementation of image manipulation and transformation functions. For practical part of this work was used library Flask using Python, HTML, Javascript. The tool was created and developed iteratively.
Vehicle Counting in Still Image
Jelínek, Zdeněk ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The main goal of this thesis was to compare different approaches to vehicle counting by density estimation. Four convolutional neural networks were tested - Counting CNN, Hydra CNN, Perspective-Aware CNN and Multi-column CNN. The evaluation of these models was done on three different datasets. The Perspective-aware CNN has achieved the most accurate results across all datasets. This model has reached 2.86 Mean Absolute Error on the PUCPR+ dataset, proving that it is the most suitable for the vehicle counting problem.
Visual control of the number of free parking spaces using cloud services
Hruban, Vladimír ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
The aim of this thesis is to design and develop cloud app service using public cloud services and computer vision to asses number of free parking spots at parking lot. I designed two possible architectures (using different potion of cloud services to run) and one of these was implemented. I also developed a web-app to handle user-interaction with the service.
Virtual Tour of FIT for Oculus Quest
Janů, Michal ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
The main goal of this work is to make an application for VR headset Oculus Quest. This application has several features that allow the user to do more than just walk freely around the FIT BUT areal, such as Navigation, Instant travel, and viewing basic room information. The Navigation feature is used to find the shortest route to the desired office or lecture room, where every room has its panel with information about the room. The instant travel feature allowes choosing a starting location.
Counting Vehicles in Image and Video
Gabzdyl, Dominik ; Herout, Adam (referee) ; Špaňhel, Jakub (advisor)
Analýza silničního provozu je stále náročnou úlohou. V průběhu této úlohy se vyskytují mnohá úskalí, která je třeba brát na vědomí. Například malé rozlišení obrazu, vysoký počet překrývajících se objektů, úhel kamery, rozmazání objektů v důsledku jejich pohybu nebo povětrnostní podmínky. Tato práce adresuje tato úskalí použitím konvolučních neuronových sítí. V této práci představuji novou architektu založenou na principu počítání regresí (Counting by Regression). Navržená architektura je inspirována některými state-of-the-art architekturami a vylepšuje přesnost na různých datasetech. Například na velmi malém PUCPR+ datasetu byla odmocnina ze střední kvadratické chyby (RMSE) snížena z 34.46 na 6.99 vozidel (měřeno na test setu). Dosažené výsledky ukázaly, že je zde stále prostor ke zlepšení a možný další výzkum v oblasti počítání regresí.
Recognition of Vehicle Class in Image
Čabala, Roman ; Kodym, Oldřich (referee) ; Špaňhel, Jakub (advisor)
The goal of this bachelor thesis is to recognize the type of vehicle from the image using neural networks. Vehicles are divided into 6 types, namely a car, a small van, a van, a mini truck, a truck and a bus. The data set was picked from videos that record the trajectory of the vehicles. Subsequently, an image annotation tool was built. The following architectures were used for network training: VGG16, ResNet50, Xception, InceptionResNet-v2. The result of the work is a comparison of architectures. All architectures were trained and achieved a result above 90%.

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