Thesis Details
Rozpoznání výrobce a modelu automobilu v obraze
This thesis focuses on training convolutional neural network for vehicle recognition in image, preparation of training data and improvement of classification accuracy. Solution focuses on effect of using 2D bounding box and data augmentation for better recognition accuracy. In this thesis, I also elaborate the comparison with papers using 3D bounding box and showing, my method approaches in some cases even outperforms method using 3D bounding box. BoxCars116k data set is used, which is freely available and collected by the GRAPH@FIT research group. In order to support the main data set, I also collected some vehicle images. As a result of the analysis, it is observed that accuracy of vehicle recognition increased 8% points in comparison with other convolutional neural networks without the proposed modifications. As part of my thesis I also performed several experiments, which show effect of different factors on classification accuracy.
vehicle recognition, convolutional neural network, data set, 2D bounding box, data augmentation, classification
Bidlo Michal, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Grégr Matěj, Ing., Ph.D. (DIFS FIT BUT), člen
Kočí Radek, Ing., Ph.D. (DITS FIT BUT), člen
@bachelorsthesis{FITBT23011,
author = "Marek Hriv\v{n}\'{a}k",
type = "Bachelor's thesis",
title = "Rozpozn\'{a}n\'{i} v\'{y}robce a modelu automobilu v obraze",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2020,
location = "Brno, CZ",
language = "slovak",
url = "https://www.fit.vut.cz/study/thesis/23011/"
}