Thesis Details

Detekce objektů pomocí hlubokých neuronových sítí

Bachelor's Thesis Student: Paníček Andrej Academic Year: 2018/2019 Supervisor: Teuer Lukáš, Ing.
English title
Deep Learning for Object Detection
Language
Czech
Abstract

This work deals with the object detection using deep neural networks. As part of the solution, I modified, implemented and trained the well-known model of cascade neural networks MTCNN so that it could perform the detection of traffic signs. The training data was generated from GTSRB and GTSDB data sets. MTCNN showed solid performance on the evaluation data, where the detection accuracy reached 97.8 %.

Keywords

neuron, deep neural network, convolutional neural network, machine learning, artificialintelligence, detection, MTCNN, traffic sign detection, GTSBD, GTSRB

Department
Department of Computer Graphics and Multimedia FIT BUT
Degree Programme
Information Technology
Files
Status
defended, grade D
Date
12 June 2019
Reviewer
Herout Adam, prof. Ing., Ph.D.
Committee
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT), předseda
Bartík Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Kočí Radek, Ing., Ph.D. (DITS FIT BUT), člen
Citation
PANÍČEK, Andrej. Detekce objektů pomocí hlubokých neuronových sítí. Brno, 2019. Bachelor's Thesis. Brno University of Technology, Faculty of Information Technology. 2019-06-12. Supervised by Teuer Lukáš. Available from: https://www.fit.vut.cz/study/thesis/22076/
BibTeX 
@bachelorsthesis{FITBT22076,
    author = "Andrej Pan\'{i}\v{c}ek",
    type = "Bachelor's thesis",
    title = "Detekce objekt\r{u} pomoc\'{i} hlubok\'{y}ch neuronov\'{y}ch s\'{i}t\'{i}",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2019,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/22076/"
}

Theses

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