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Traffic sign detection in real time
Sicha, Marek ; Přinosil, Jiří (referee) ; Bravenec, Tomáš (advisor)
The bachelor's thesis focuses on the detection and classification of traffic signs in images and video sequences. The goal of the work is also the possibility to perform detection and classification on a single board computer. Neural networks and the Python programming language were chosen to solve the problem. Object detection and classification are solved separately, so two neural networks were used. A convolutional neural network was chosen for classification and a detector from the EfficientDet family was chosen for detection. The overall architecture was tested on a single board Nvidia Jetson Nano computer.
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Parking Spot Recognition by Artificial Intelligence
Sicha, Marek ; Koudelka, Vlastimil (referee) ; Kadlec, Petr (advisor)
This thesis deals with the recognition of parking spots in images using artificial intelligence. The goal of the work was to study neural networks and select a suitable network to solve the problem. Python programming language was chosen for implementation and Mask R-CNN convolutional network was selected as a suitable neural network. To train the neural network, a custom dataset was created which contains images captured from street cameras. The trained network was then implemented in a program that easily provides information about available parking spaces in a particular area. The program analyzes images from cameras in parking lots and on streets, identifies the number of available parking spaces, and displays this information on a map.
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Traffic Sign Classification Using Deep Learning
Sicha, Marek
The thesis focuses on the classification of traffic signs in images and video sequences.The goal is real-time processing and usage of software in the vehicle. Neural networks and thePython programming language were chosen to solve the problem. To solve the problem a machinelearning method was chosen, more precisely a convolutional neural network. A neural network inthe Python programming language was created for the classification of traffic signs, using the Kerasand Tensorflow libraries. The neural network architecture is chosen for optimization for use on asingle-board computer with limited performance.
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Traffic sign detection in real time
Sicha, Marek ; Přinosil, Jiří (referee) ; Bravenec, Tomáš (advisor)
The bachelor's thesis focuses on the detection and classification of traffic signs in images and video sequences. The goal of the work is also the possibility to perform detection and classification on a single board computer. Neural networks and the Python programming language were chosen to solve the problem. Object detection and classification are solved separately, so two neural networks were used. A convolutional neural network was chosen for classification and a detector from the EfficientDet family was chosen for detection. The overall architecture was tested on a single board Nvidia Jetson Nano computer.
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Depozice tenkých vrstev Ba.sub.x./sub.Sr.sub.1-x./sub.TiO.sub.3./sub. pomocí dvoutryskového RF plazmatického systému s efektem duté katody
Hubička, Zdeněk ; Virostko, Petr ; Olejníček, Jiří ; Deyneka, Alexander ; Adámek, Petr ; Valvoda, V. ; Jastrabík, Lubomír ; Šícha, Miloš ; Tichý, M.
Low-pressure plasma jet system with two hollow cathodes was used for deposition of BaxSr1-xTiO3 (BSTO) ferroelectric thin films. High density pulse modulated RF hollow cathode discharge was generated in ceramic nozzles made of SrTiO3 and BaTiO3. In the experiment, the composition of BSTO films was controlled by magnitude of average RF power applied on particular plasma jet guns. X-ray diffraction proved that BSTO thin films were polycrystalline with perovskite structure. Time resolved Langmuir probe system and plasma impedance monitor were used for ´in situ´ plasma diagnostics and control of deposition conditions.
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