The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2022.0130935
PDF

Real Time Fire Detection using Color Probability Segmentation and DenseNet Model for Classifier

Author 1: Faisal Dharma Adhinata
Author 2: Nur Ghaniaviyanto Ramadhan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: The forest is an outdoor environment not touched by the surrounding community, so it is not immediately handled when a fire occurs. Therefore, surveillance using cameras is needed to see the presence of fire hotspots in the forest. This study aims to detect hotspots through video data. As is known, fire has a variety of colors, ranging from yellow to reddish. The segmentation process requires a method that can recognize various fire colors to get a candidate fire object area in the video frame. The methods used for the color segmentation process are Gaussian Mixture Model (GMM) and Expectation–maximization (EM). The segmentation results are candidates for fire areas, which in the experiment used the value of K=4. This fire object candidate needs to be ascertained whether the segmented object is a fire object or another object. In the feature extraction stage, this research uses the DenseNet-169 or DenseNet-201 models. In this study, various color tests were carried out, namely RGB, HSV, and YCbCr. The test results show that RGB color produces the most optimal training accuracy. This RGB color configuration is used to test using video data. The test results show that the true positive and false negative values are quite good, 98.69% and 1.305%. This video data processing produces fps with an average of 14.43. So, it can be said that this combination of methods can be used to process real time data in case studies of fire detection.

Keywords: Fire detection; color segmentation; GMM-EM; DenseNet; real time

Faisal Dharma Adhinata and Nur Ghaniaviyanto Ramadhan, “Real Time Fire Detection using Color Probability Segmentation and DenseNet Model for Classifier” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130935

@article{Adhinata2022,
title = {Real Time Fire Detection using Color Probability Segmentation and DenseNet Model for Classifier},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130935},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130935},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Faisal Dharma Adhinata and Nur Ghaniaviyanto Ramadhan}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org