Objective detection and scene understanding based on deep learning
Machine vision plays a more and more important role in the industrial and medical market. In order to promote the progress of human society, it is necessary to deeply study artificial intelligence and deep network. In some important applications, such as automatic driving and medical detection, it w...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/158405 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-158405 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1584052023-07-07T19:32:14Z Objective detection and scene understanding based on deep learning Li, Zeyin Jiang Xudong School of Electrical and Electronic Engineering EXDJiang@ntu.edu.sg Engineering::Electrical and electronic engineering Machine vision plays a more and more important role in the industrial and medical market. In order to promote the progress of human society, it is necessary to deeply study artificial intelligence and deep network. In some important applications, such as automatic driving and medical detection, it will be applied to target detection and semantic segmentation based on deep learning. In order to better understand a scene, one is to identify the goals we care about in the scene, and the other is to identify the categories of each part of the scene class. This involves the semantic segmentation and target detection. This project makes an in-depth study on scene understanding in three dimensions: theoretical analysis, code construction and performance comparison. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-06-04T05:45:08Z 2022-06-04T05:45:08Z 2022 Final Year Project (FYP) Li, Z. (2022). Objective detection and scene understanding based on deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158405 https://hdl.handle.net/10356/158405 en W3348-212 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 Li, Zeyin Objective detection and scene understanding based on deep learning |
description |
Machine vision plays a more and more important role in the industrial and medical market. In order to promote the progress of human society, it is necessary to deeply study artificial intelligence and deep network. In some important applications, such as automatic driving and medical detection, it will be applied to target detection and semantic segmentation based on deep learning. In order to better understand a scene, one is to identify the goals we care about in the scene, and the other is to identify the categories of each part of the scene class. This involves the semantic segmentation and target detection. This project makes an in-depth study on scene understanding in three dimensions: theoretical analysis, code construction and performance comparison. |
author2 |
Jiang Xudong |
author_facet |
Jiang Xudong Li, Zeyin |
format |
Final Year Project |
author |
Li, Zeyin |
author_sort |
Li, Zeyin |
title |
Objective detection and scene understanding based on deep learning |
title_short |
Objective detection and scene understanding based on deep learning |
title_full |
Objective detection and scene understanding based on deep learning |
title_fullStr |
Objective detection and scene understanding based on deep learning |
title_full_unstemmed |
Objective detection and scene understanding based on deep learning |
title_sort |
objective detection and scene understanding based on deep learning |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/158405 |
_version_ |
1772826219188846592 |