Machine Learning Applications for Fire Detection in a Residential Building

Ngonidzashe A. Mwedzi, Nnamdi I. Nwulu, Saheed Lekan Gbadamosi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Fire is one of the most serious accidents that can occur in houses, schools, offices and companies. This can lead to several losses, causalities and serious equipment damages. It is highly essential to put in place advanced disaster response mechanisms in order to safeguard against fire disaster in our environment. Recently, modern buildings possess surveillance cameras for security purpose, such cameras can be utilized for fire detection in buildings. In this paper, deep learning and computer vision are applied for detecting fire incident in different systems. The proposed model utilizes an advanced image processing and classification algorithms via deep learning and convolutional neural networks (CNN) to improve the performance of residential fire alarms and eradicate nuisance alarm scenarios.

Original languageEnglish
Title of host publicationICETAS 2019 - 2019 6th IEEE International Conference on Engineering, Technologies and Applied Sciences
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728140827
DOIs
Publication statusPublished - Dec 2019
Event6th IEEE International Conference on Engineering, Technologies and Applied Sciences, ICETAS 2019 - Kuala Lumpur, Malaysia
Duration: 20 Dec 201921 Dec 2019

Publication series

NameICETAS 2019 - 2019 6th IEEE International Conference on Engineering, Technologies and Applied Sciences

Conference

Conference6th IEEE International Conference on Engineering, Technologies and Applied Sciences, ICETAS 2019
Country/TerritoryMalaysia
CityKuala Lumpur
Period20/12/1921/12/19

Keywords

  • Deep Learning
  • false alarm
  • fire detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Energy Engineering and Power Technology
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation

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