Fire Detection Using Image Processing
release_iapgaqdwqjb33chyozb2ti3sp4
by
B. Swarajya Lakshmi
2021 Volume 10, p14-19
Abstract
Fire disasters have always been a threat to homes and businesses even with the various systems in place to prevent them. They cause property damage, injuries and even death. Preparedness is vital when dealing with fires. They spread uncontrollably and are difficult to contain. To contain them it is necessary for the fire to be detected early. Image fire detection heavily relies on an algorithmic analysis of images. However, the accuracy is lower, the detection is delayed and in common detection algorithms a large number of computation, including the image features being extracted manually and using machine. Therefore, in this paper, novel image detection which will be based on the advanced object detection like CNN model of YOLO v3 is proposed. The average precision of the algorithm based on YOLO v3 reaches to 81.76% and also it has the stronger robustness of detection performance, thereby satisfying the requirements of the real-time detection.
In application/xml+jats
format
Archived Files and Locations
application/pdf 1.0 MB
file_ut5iy2wqbzcizm53wjnlojre6a
|
ojs.trp.org.in (publisher) web.archive.org (webarchive) |
article-journal
Stage
published
Date 2021-11-05
access all versions, variants, and formats of this works (eg, pre-prints)
Crossref Metadata (via API)
Worldcat
SHERPA/RoMEO (journal policies)
wikidata.org
CORE.ac.uk
Semantic Scholar
Google Scholar