TELKOMNIKA Telecommunication Computing Electronics and Control
An automatic flame detection system for outdoor areas
Dublin Core
Title
TELKOMNIKA Telecommunication Computing Electronics and Control
An automatic flame detection system for outdoor areas
An automatic flame detection system for outdoor areas
Subject
Area detection
Frame differences
HSV/YCbCr color space
Image processing
Otsu’s algorithm
Wavelet transforms
Frame differences
HSV/YCbCr color space
Image processing
Otsu’s algorithm
Wavelet transforms
Description
Traditional fire detection depends on smoke sensors. This strategy, however,
is unsuited for big and open buildings, as well as outdoor regions. As a
result, based on computer vision systems, this research proposes an effective
method for recognizing flames in open areas. To minimize data size without
losing important information, integer Haar lifting wavelet transform is used
to frame and analyze the input video. Then, three color spaces (binary, hue,
saturation, value (HSV), and YCbC) are used in simultaneous color
detection. In binary space, Otsu’s approach is utilized to determine
automated intensity pixels. Additionally, using frame differences to reduce
false alarms. According to the experimental results, the approach achieves
99% accuracy for offline videos and surpasses 93% accuracy for real-time
videos while maintaining a lower level of complexity.
is unsuited for big and open buildings, as well as outdoor regions. As a
result, based on computer vision systems, this research proposes an effective
method for recognizing flames in open areas. To minimize data size without
losing important information, integer Haar lifting wavelet transform is used
to frame and analyze the input video. Then, three color spaces (binary, hue,
saturation, value (HSV), and YCbC) are used in simultaneous color
detection. In binary space, Otsu’s approach is utilized to determine
automated intensity pixels. Additionally, using frame differences to reduce
false alarms. According to the experimental results, the approach achieves
99% accuracy for offline videos and surpasses 93% accuracy for real-time
videos while maintaining a lower level of complexity.
Creator
Zahraa Shihab Al Hakeem, Haider Ismael Shahadi, Hawraa Hassan Abass
Source
http://telkomnika.uad.ac.id
Date
Feb 16, 2023
Contributor
peri irawan
Format
pdf
Language
english
Type
english
Files
Collection
Citation
Zahraa Shihab Al Hakeem, Haider Ismael Shahadi, Hawraa Hassan Abass, “TELKOMNIKA Telecommunication Computing Electronics and Control
An automatic flame detection system for outdoor areas,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4572.
An automatic flame detection system for outdoor areas,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4572.