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...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/76920 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-76920 |
---|---|
record_format |
dspace |
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 |
_version_ |
1759855970636267520 |