Thesis Details

Detekce graffiti tagů v obraze

Bachelor's Thesis Student: Molisch Marek Academic Year: 2020/2021 Supervisor: Špaňhel Jakub, Ing.
English title
Detection of Graffiti Tags in Image
Language
Czech
Abstract

The goal of this work is to compare today's architecture of object detection models and use them for the purpose of graffiti tag detection. State-of-the-art models, which are compatible with the Tensorflow framework, were used. Faster R-CNN architecture was found to be the most accurate and SSD architecture to be the fastest. Experiments with graffiti tags from Athens in the STORM dasater showed, that it is better to approach graffiti tags as objects rather than writings.

Keywords

object detection, graffiti tags, convolutional neural networks, Faster R-CNN, SSD, CenterNet, EfficientDet, Tensorflow

Department
Department of Computer Graphics and Multimedia FIT BUT
Degree Programme
Information Technology
Files
Status
defended, grade B
Date
14 June 2021
Reviewer
Herout Adam, prof. Ing., Ph.D.
Committee
Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT), předseda
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Matoušek Petr, doc. Ing., Ph.D., M.A. (DIFS FIT BUT), člen
Citation
MOLISCH, Marek. Detekce graffiti tagů v obraze. Brno, 2021. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2021-06-14. Supervised by Špaňhel Jakub. Available from: https://www.fit.vut.cz/study/thesis/23051/
BibTeX 
@bachelorsthesis{FITBT23051,
    author = "Marek Molisch",
    type = "Bachelor's thesis",
    title = "Detekce graffiti tag\r{u} v obraze",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2021,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/23051/"
}

Theses

Back to top