|
|
Klasifikace dat v obraze pomocí nástrojů pro strojové učení v jazyce Python
Voronin, Artyom ; Appel, Martin (referee) ; Bastl, Michal (advisor)
This thesis introduces the issue of data classification in the image using tools for machine learning in Python. The aim is to verify the possibilities of overtraining existing models on their own data and evaluating the efficiency and complexity of the entire process. Subsequently, the processing of the achieved results in the form of a demonstration task, image capturing by a web camera and classification of the object in the field of view.
|
|
|
Detection and classification of objects of interest for watering mobile robot using image processing
Sladký, Jiří ; Šnajder, Jan (referee) ; Krejsa, Jiří (advisor)
This thesis deals with image processing on autonomous watering mobile robot using embedded computer NVIDIA Jetson Nano. A method for object detection, YOLOv5, was chosen, which served for detection of flowers and flower pots. Using a method for monocular depth estimation, MiDaS, relative depth map was predicted. An algorithm was created, which converted this map to metric depth map using data from LiDAR. Thanks to that, distance of the detected flowers could be estimated. The created tools were implemented in ROS framework and tested on real data form indoor environment.
|
|
|
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.
|
|
|
Detection of Traffic Signs in Image and Video
Kočica, Filip ; Hradiš, Michal (referee) ; Herout, Adam (advisor)
This thesis deals with the traffic sign detection problematics using modern techniques in image processing. Special architecture of deep convolutional neural network YOLO, i.e. You Only Look Once, which performs both detection and classification in one step, has been used. This architecture allows object detector to work on very high speeds. This thesis also deals with comparison of models trained on real and synthetic datasets. The best model trained on real dataset has reached 63.4% mAP success rate and 82.3% mAP when trained on synthetic dataset. Evaluation of one image takes about ~40.4ms on average graphics processing unit and ~3.9ms on higher than average graphics processing unit. The benefit of this thesis is that under certain conditions neural network model trained on synthetic data can achieve same or even better results than model trained on real data. This may simplify process of object detector development since it is not necessary to annotate large number of images.
|
|
|
Deep-learning-based pattern detection in medical images
Koščová, Zuzana ; Vičar, Tomáš (referee) ; Jakubíček, Roman (advisor)
This Bachelor thesis deals with Deep-learning-based pattern detection in medical images. For better understanding of a subject artificial neural network and convolutional neural network (CNN) are described at first. Next chapter is focused on specific detection methods which use CNN. Within a bachelor thesis a dataset of abdominal CT a MRI scans was created. Faster R-CNN and YOLO algorithms were trained and tested on acquired scans for liver detection. Implementation of chosen methods took place in Python programming language using the Pytorch library. Finally, detection results and possible use in medicine are discussed.
|
|
|
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. Práca si ďalej dáva za cieľ využiť dostupné riešenia čiastkových problémov: detekcia osôb v obraze, identifikácie 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 osôb v zábere kamery a príslušne ho prezentovať.
|
|
|
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.
|
|
|
Automatic Trafic Scene Analysis Using Image Processing
Válek, Lukáš ; Špaňhel, Jakub (referee) ; Zemčík, Pavel (advisor)
Tato práce se zabývá problematikou analýzy scény pomocí metod počítačového vidění. Cílem této práce je vytvořit systém schopný automaticky detekovat anomálie nacházející se ve video záznamech. Práce se zabývá systémy pro detekci a sledování objektů v obraze, tvorbou grafického uživatelského rozhraní a algoritmem pro detekci porušení uživatelem definovaných pravidel. Výsledkem práce je webová aplikace, která uživateli umožňuje správu videozáznamů, definování pravidel pro scénu, zahájení detekce anomálií a zobrazení výsledků analýzy. Systém pracuje v reálném čase, upozorňuje uživatele o dokončení operace a uchovává výsledky analýzy pro další zpracovaní.
|
|
|
Detection of Traffic Signs and Lights
Chocholatý, Tomáš ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The thesis focuses on traffic sign detection and traffic lights detection in view with utilization convolution neural network. The goal is create suitable detector for detection and classification traffic sign in real traffic. For training of convolution neural network were created appropriate datasets, that contains synthetic and real dataset. For synthetic dataset was create generator, that can simulated different deformation of traffic signs. Evaluation is done by own program for quantitative evaluation. The detection rate successfully detected signs is 89\% over own test dataset. The results allow to find out importance of representation real or synthetic dataset in training dataset and influence individual deformations synthetic dataset for final detection quality.
|
|
|
Detection of Boxes in Image
Soroka, Matej ; Zlámal, Adam (referee) ; Herout, Adam (advisor)
The aim of this work is to experiment and evaluate algorithms with different approaches to computer vision in order to automatically detect boxes-blocks in the image. To this end, neural network-based approaches 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 final iteration the use of U-net network for detection of boxes in the image.
|
guest ::
