Loading...
Computer vision techniques for fire detection and localization
Haridasan, Smitha
Haridasan, Smitha
Citations
Altmetric:
Files
Loading...
Abstract
Adobe PDF, 55.45 KB
Authors
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
3/29/2022
Type
Abstract
Genre
Keywords
Subjects (LCSH)
Citation
Abstract
From droughts, earthquakes, tornadoes, winter storms and wildfires, communities in Kansas have
weathered significant damage over the past. During 2016, 2017 and 2021, record breaking wildfires
burned thousands of acres in Kansas and placed significant demands on state's local fire
department. In December 2021, Four County Fire had burned an area approximately close to 96,000
acres. Strong winds in Kansas made these wildfires uncontrollable burning down houses and
businesses on its path. Strikes, arson, sparks from vehicles, or prescribed burns escaping control
are causes for wildfires to burn down houses, barns and fences resulting in both human and animal
deaths. Many wildfires remain small and are easily contained, but some grow rapidly and require
significant suppression efforts and resources. While fire fighters try to control fast spreading fires,
smoke travels miles causing health concerns. Windstorm and wildfires cause millions worth of
damage in Kansas. As global climate gets warmer, probability and intensity of forest fires will
gradually increase, therefore it is important to intelligently monitor forest fires. To meet the needs
of hazard monitoring drones and helicopters, multi-spectral light weight deep learning algorithms
have proven to be very effective in early detection of fire in images captured from drones with an
accuracy of 97.70%.
Table of Contents
Description
Poster project completed at the Wichita State University DRAC: Disaster Resilience Analytics Center. Presented at the 19th Annual Capitol Graduate Research Summit, Topeka, KS, March 29, 2022.
Publisher
Wichita State University