Thesis Details
Detekce vozidla v obraze
This bachelor thesis is focused on vehicle detection. The thesis deals with the method of vehicle detection using convolutional neural networks, their structures and models. All scripts were implemented using python programming language with Tensorflow Object Detection API interface. The first part of this thesis was devote to the structures of popular neural networks and models of detection neural networks. The next chapter deals with the most famous frameworks that are used for machine learning. Three neural network models were selected and trained on the COD20K dataset. The result of this thesis is statistics that discuss the efficiency and performance of each model on trained dataset and compare performance without displaying video on Nvidia RTX 2060, where the performace archieved by SSD MobileNet V2 network was 300FPS and Nvidia Tegra TX2 8GB, whose performace reached almost 44FPS.
vehicle detection, convolutional neural networks, Python, TensorFlow, Nvidia Tegra, TensorRt, quantization, R-CNN, SSD, ONNX
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Burget Radek, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Jaroš Jiří, doc. Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT23204,
author = "Adam Petr\'{a}\v{s}",
type = "Bachelor's thesis",
title = "Detekce vozidla v obraze",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2020,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/23204/"
}