Deep learning for aerial image analysis

This project explores the topic of deep learning and how to implement it onto image analysis, namely image classification and image detection. The topic of focus would be the classification and detection of wildfires as images could be captured from drones to fulfill the aerial image segment of the...

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Main Author: Lim, Benjamin Hong Siong
Other Authors: Lin Guosheng
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/76920
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-769202023-03-03T20:46:29Z Deep learning for aerial image analysis Lim, Benjamin Hong Siong Lin Guosheng School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering This project explores the topic of deep learning and how to implement it onto image analysis, namely image classification and image detection. The topic of focus would be the classification and detection of wildfires as images could be captured from drones to fulfill the aerial image segment of the project. As climate change is a growing issue today, we bring our attention to one of the causes, global warming due to deforestation by fire. This can be caused both naturally due to high temperatures or by men using irresponsible slash and burn methods for farming. As current ways of detecting wildfires is still lacking, the drive of the project would be to explore wildfire detection through image recognition. The first half of the project explores object classification and how to classify an image as one that contains a wildfire and one that does not. In the second half, upon classifying the image as a wildfire, an image detection algorithm will be ran to “locate” the fire. This project also experiments on different models to see which ones produces the best results for both techniques. Bachelor of Engineering (Computer Science) 2019-04-23T14:14:15Z 2019-04-23T14:14:15Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/76920 en Nanyang Technological University 42 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Lim, Benjamin Hong Siong
Deep learning for aerial image analysis
description This project explores the topic of deep learning and how to implement it onto image analysis, namely image classification and image detection. The topic of focus would be the classification and detection of wildfires as images could be captured from drones to fulfill the aerial image segment of the project. As climate change is a growing issue today, we bring our attention to one of the causes, global warming due to deforestation by fire. This can be caused both naturally due to high temperatures or by men using irresponsible slash and burn methods for farming. As current ways of detecting wildfires is still lacking, the drive of the project would be to explore wildfire detection through image recognition. The first half of the project explores object classification and how to classify an image as one that contains a wildfire and one that does not. In the second half, upon classifying the image as a wildfire, an image detection algorithm will be ran to “locate” the fire. This project also experiments on different models to see which ones produces the best results for both techniques.
author2 Lin Guosheng
author_facet Lin Guosheng
Lim, Benjamin Hong Siong
format Final Year Project
author Lim, Benjamin Hong Siong
author_sort Lim, Benjamin Hong Siong
title Deep learning for aerial image analysis
title_short Deep learning for aerial image analysis
title_full Deep learning for aerial image analysis
title_fullStr Deep learning for aerial image analysis
title_full_unstemmed Deep learning for aerial image analysis
title_sort deep learning for aerial image analysis
publishDate 2019
url http://hdl.handle.net/10356/76920
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