Vision-based smoke detector

Vision-based smoke detector

Previous studies have documented the significant applications of the electronic smoke detector. With the capabilities of vision based fire detection and increase in the number of surveillance cameras, a lesser attention is given to the vision-based type smoke detector. Moreover, some drawbacks have...

Full description

Bibliographic Details
Main Authors: Abdullah, Ali Mohammed Noman, Htike@Muhammad Yusof, Zaw Zaw
Format: Article
Language:English
English
Published: Science Publishing Corporation 2019
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/73168/
http://irep.iium.edu.my/73168/1/Vision%20Based%20Smoke%20detector%20%282%29.pdf
http://irep.iium.edu.my/73168/2/Vision-based%20acceptance.pdf
id iium-73168
recordtype eprints
spelling iium-731682019-07-15T02:15:50Z http://irep.iium.edu.my/73168/ Vision-based smoke detector Abdullah, Ali Mohammed Noman Htike@Muhammad Yusof, Zaw Zaw T Technology (General) Previous studies have documented the significant applications of the electronic smoke detector. With the capabilities of vision based fire detection and increase in the number of surveillance cameras, a lesser attention is given to the vision-based type smoke detector. Moreover, some drawbacks have been identified in the accuracy and efficiency of smoke detection. The present study proposes a vision based smoke detector to overcome the shortcomings of the current traditional electronic and vision based smoke detectors. A Convolutional Neural Network is used to classify the smoke regions. After testing the proposed method, the accuracy was approximately 94%. When a modern approach of object detection is used to support image classifying, its accuracy increases by 96%. Science Publishing Corporation 2019 Article PeerReviewed application/pdf en http://irep.iium.edu.my/73168/1/Vision%20Based%20Smoke%20detector%20%282%29.pdf application/pdf en http://irep.iium.edu.my/73168/2/Vision-based%20acceptance.pdf Abdullah, Ali Mohammed Noman and Htike@Muhammad Yusof, Zaw Zaw (2019) Vision-based smoke detector. International Journal of Engineering & Technology. ISSN 2227-524X (In Press)
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Abdullah, Ali Mohammed Noman
Htike@Muhammad Yusof, Zaw Zaw
Vision-based smoke detector
description Previous studies have documented the significant applications of the electronic smoke detector. With the capabilities of vision based fire detection and increase in the number of surveillance cameras, a lesser attention is given to the vision-based type smoke detector. Moreover, some drawbacks have been identified in the accuracy and efficiency of smoke detection. The present study proposes a vision based smoke detector to overcome the shortcomings of the current traditional electronic and vision based smoke detectors. A Convolutional Neural Network is used to classify the smoke regions. After testing the proposed method, the accuracy was approximately 94%. When a modern approach of object detection is used to support image classifying, its accuracy increases by 96%.
format Article
author Abdullah, Ali Mohammed Noman
Htike@Muhammad Yusof, Zaw Zaw
author_facet Abdullah, Ali Mohammed Noman
Htike@Muhammad Yusof, Zaw Zaw
author_sort Abdullah, Ali Mohammed Noman
title Vision-based smoke detector
title_short Vision-based smoke detector
title_full Vision-based smoke detector
title_fullStr Vision-based smoke detector
title_full_unstemmed Vision-based smoke detector
title_sort vision-based smoke detector
publisher Science Publishing Corporation
publishDate 2019
url http://irep.iium.edu.my/73168/
http://irep.iium.edu.my/73168/1/Vision%20Based%20Smoke%20detector%20%282%29.pdf
http://irep.iium.edu.my/73168/2/Vision-based%20acceptance.pdf
first_indexed 2023-09-18T21:43:45Z
last_indexed 2023-09-18T21:43:45Z
_version_ 1777413312269516800

Similar Items