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Mushroom Detection and Recognition in Natural Environment
Steinhauser, Dominik ; Juránek, Roman (referee) ; Špaňhel, Jakub (advisor)
In this thesis is handled the problem of mushroom detection and recognition in natural environment. Convolutional neural networks are used. The beginning of this thesis is dedicated to the theory of neural networks. Further is solved the problem of object detection and classification. Using neural network trained for classification is solved also the task of localization. Results of trained CNNs are analised.
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Convolutional neural networks for identification of axial 2D slices in CT data
Vavřinová, Pavlína ; Harabiš, Vratislav (referee) ; Jakubíček, Roman (advisor)
This thesis deals with the classification of axial 2D slices in CT patient’s data into six categories. The sphere of convolutional neural networks was used for this purpose. For a better understanding of this issue, the basics of neural networks and then the principles of deep learning including convolutional neural networks are explained at first. The AlexNet network was specifically selected for the intention of this identification, and it was tested on the created data set after being adaptated. The overall classification success rate was 86% ,after the final adjustments, a slight improvement was achieved and the identification success rate was 87%.
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Deep Learning for Image Recognition
Munzar, Milan ; Kolář, Martin (referee) ; Hradiš, Michal (advisor)
Neural networks are one of the state-of-the-art models for machine learning today. One may found them in autonomous robot systems, object and speech recognition, prediction and many others AI tasks. The thesis describes this model and its extension which is used in an object recognition. Then explains an application of a convolutional neural networks(CNNs) in an image recognition on Caltech101 and Cifar10 datasets. Using this exemplar application, the thesis discusses and measures efficiency of techniques used in CNNs. Results show that the convolutional networks without advanced extensions are able to reach a 80\% recognition accuracy on Cifar-10 and a 37\% accuracy on Caltech101.
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Evaluation of targets in shooting range based on image data
Sujová, Sára ; Šťastný, Jiří (referee) ; Škrabánek, Pavel (advisor)
The thesis describes the design and implementation of a computer vision system for evaluating targets on the shooting range using image data. The program respects the restrictions based on safety measures established by the the shooting range manager and uses an uniform system of lighting and camera placement. The work consists of several parts. The first part is the creation of the dataset and its annotation. The second part is the creation of the program. The program includes a photo of the target, which is suitably edited and divided into sub-areas in the pre-processing phase. These sub-regions are then iteratively processed by the U-NET network, which produces segmentation maps that are subsequently combined into the resulting map. The positions of the detected shots are obtained from this map. In the last part of the program, a point evaluation of the shooting session is obtained.
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Object Detection in the Laser Scans Using Convolutional Neural Networks
Marko, Peter ; Beran, Vítězslav (referee) ; Veľas, Martin (advisor)
This thesis is aimed at detection of lines of horizontal road markings from a point cloud, which was obtained using mobile laser mapping. The system works interactively in cooperation with user, which marks the beginning of the traffic line. The program gradually detects the remaining parts of the traffic line and creates its vector representation. Initially, a point cloud is projected into a horizontal plane, crating a 2D image that is segmented by a U-Net convolutional neural network. Segmentation marks one traffic line. Segmentation is converted to a polyline, which can be used in a geo-information system. During testing, the U-Net achieved a segmentation accuracy of 98.8\%, a specificity of 99.5\% and a sensitivity of 72.9\%. The estimated polyline reached an average deviation of 1.8cm.
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Vehicle License Plate Detection and Recognition Software
Masaryk, Adam ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The aim of this bachelor thesis is to design and develop software that can detect and recognize license plates from images. The software is divided into 3 parts - license plates detection, detector output processing and license plates characters recognition. We decided to implement detection and recognition using modern methods using convolutional neural networks.
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Detection of Graffiti Tags in Image
Pavlica, Jan ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The thesis is focused on the possible utilization of current methods in the area of computer vision with the purpose of automatic detection of graffiti tags in the image. Graffiti tagsare the most common expression of graffiti, which serves as the author’s signature. In the thesis, state-of-the-art detection systems were tested; the most effective one is the Single Shot MultiBox Detector. The result has reached 75.7% AP.
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Counting Crates in Images
Mičulek, Petr ; Špaňhel, Jakub (referee) ; Herout, Adam (advisor)
V této práci se zabývám tématem počítání beden v obrazových datech pomocí technik hlubokého učení. V práci jsem navrhl řešení pro počítání beden, které představuji na fotkách krabiček sirek. Ačkoli původní řešení počítalo s využitím datové sady beden ze skladu pivovaru, sada nakonec nebyla dodána a na doporučení vedoucího práce byly pro řešení vybrány bloky krabiček sirek. Implementované řešení využívá plně konvoluční neuronovou síť založenou na klasifikaci, umožňující výstup ve vysokém rozlišení. Tato síť je trénována na výřezech fotek z datové sady, díky čemuž je řešení rychlé a síť je vhodná i pro použití na menších datových sadách. Síť detekuje ve fotkách klíčové body krabiček sirek, které jsou následně zpracovány algoritmem pro odhad klíčových bodů z predikce sítě a výpočet finálního počtu beden. Na validačním datasetu dosahuje řešení následujících výsledků: ve 12,5 % případů predikce selže a ve zbylých případech má průměrnou absolutní odchylku (MAE) 11,14. Pomocí rozsáhlých experimentů bylo řešení vyhodnoceno a výsledky potvrzují, že tento přístup může být použit pro počítání objektů.
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Automatic Industrial Quality Control from Image
Kruták, Martin ; Hradiš, Michal (referee) ; Zemčík, Pavel (advisor)
The goal of this thesis is to create overall, automatic and non-contact quality control of a pellet. The issue is divided into two separate parts. The first part deals with precise dimensional measuring of pellet - its length and head diameter so that it is precise and reasonably fast. Precise measuring is achieved with help of algorithms which achieve the sub-pixel precision by polynomial approximation of the edges extracted from the image gradients. The second part deals with the defects of a pellet. Detecting defects like longitudinal furrows or skirt cuts is achieved with convolutional neural networks. The measurement modules work with the resulting precision up to 0.025 mm in case of length measuring and up to 0.01 mm in case of head diameter measuring. In case of defect detections, neural network shows very high classification success rate. The contribution of this thesis is a presentation of innovative approaches in automatic quality control of pellets with use of neural networks and a demonstration of its usage in real manufacturing process.
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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.
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