Scene understanding for unmanned vehicle using deep learning

In this dissertation, I learned some mainstream deep learning algorithms for scene understanding in the world. By comparing the performance of these algorithms on the PLACE365 dataset, finally proposed a method to use the Transformer layer as a pooling layer, and applied it to some mainstream deep l...

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Bibliographic Details
Main Author:	Lu, Yizhou
Other Authors:	Wang Dan Wei
Format:	Thesis-Master by Coursework
Language:	English
Published:	Nanyang Technological University 2022
Subjects:	Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/155393
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Institution:	Nanyang Technological University
Language:	English

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record_format	dspace
spelling	sg-ntu-dr.10356-1553932023-07-04T17:09:06Z Scene understanding for unmanned vehicle using deep learning Lu, Yizhou Wang Dan Wei School of Electrical and Electronic Engineering EDWWANG@ntu.edu.sg Engineering::Electrical and electronic engineering In this dissertation, I learned some mainstream deep learning algorithms for scene understanding in the world. By comparing the performance of these algorithms on the PLACE365 dataset, finally proposed a method to use the Transformer layer as a pooling layer, and applied it to some mainstream deep learning networks for testing. By applying this method to the more classic neural network models such as Resnet18, Alexnet and VGG16, experiments on different datasets have shown that this method has a greater improvement in the accuracy of the model. Master of Science (Computer Control and Automation) 2022-02-22T00:27:17Z 2022-02-22T00:27:17Z 2021 Thesis-Master by Coursework Lu, Y. (2021). Scene understanding for unmanned vehicle using deep learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/155393 https://hdl.handle.net/10356/155393 en application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering
spellingShingle	Engineering::Electrical and electronic engineering Lu, Yizhou Scene understanding for unmanned vehicle using deep learning
description	In this dissertation, I learned some mainstream deep learning algorithms for scene understanding in the world. By comparing the performance of these algorithms on the PLACE365 dataset, finally proposed a method to use the Transformer layer as a pooling layer, and applied it to some mainstream deep learning networks for testing. By applying this method to the more classic neural network models such as Resnet18, Alexnet and VGG16, experiments on different datasets have shown that this method has a greater improvement in the accuracy of the model.
author2	Wang Dan Wei
author_facet	Wang Dan Wei Lu, Yizhou
format	Thesis-Master by Coursework
author	Lu, Yizhou
author_sort	Lu, Yizhou
title	Scene understanding for unmanned vehicle using deep learning
title_short	Scene understanding for unmanned vehicle using deep learning
title_full	Scene understanding for unmanned vehicle using deep learning
title_fullStr	Scene understanding for unmanned vehicle using deep learning
title_full_unstemmed	Scene understanding for unmanned vehicle using deep learning
title_sort	scene understanding for unmanned vehicle using deep learning
publisher	Nanyang Technological University
publishDate	2022
url	https://hdl.handle.net/10356/155393
_version_	1772828372189052928

Scene understanding for unmanned vehicle using deep learning

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