Thesis Details
Detekce dopravních značek a semaforů
The thesis focuses on traffic sign detection and traffic lights detection in view with utilization convolution neural network. The goal is create suitable detector for detection and classification traffic sign in real traffic. For training of convolution neural network were created appropriate datasets, that contains synthetic and real dataset. For synthetic dataset was create generator, that can simulated different deformation of traffic signs. Evaluation is done by own program for quantitative evaluation. The detection rate successfully detected signs is 89\% over own test dataset. The results allow to find out importance of representation real or synthetic dataset in training dataset and influence individual deformations synthetic dataset for final detection quality.
Traffic sign detection and classification, Convolution neural network, Object detecion, YOLO, Synthetic dataset, Generator for synthetic dataset, Quantitative evaluation
Fusek Michal, Ing., Ph.D. (DMAT FEEC BUT), člen
Martínek Tomáš, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Matoušek Petr, doc. Ing., Ph.D., M.A. (DIFS FIT BUT), člen
Rogalewicz Adam, doc. Mgr., Ph.D. (DITS FIT BUT), člen
@bachelorsthesis{FITBT20883,
author = "Tom\'{a}\v{s} Chocholat\'{y}",
type = "Bachelor's thesis",
title = "Detekce dopravn\'{i}ch zna\v{c}ek a semafor\r{u}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2019,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/20883/"
}