Forest Fire Identification in UAV Imagery Using X-MobileNet

Forest fires are caused naturally by lightning, high atmospheric temperatures, and dryness. Forest fires have ramifications for both climatic conditions and anthropogenic ecosystems. According to various research studies, there has been a noticeable increase in the frequency of forest fires in India...

Full description

Saved in:
Bibliographic Details
Published in:Electronics (Basel) 2023-02, Vol.12 (3), p.733
Main Authors: Namburu, Anupama, Selvaraj, Prabha, Mohan, Senthilkumar, Ragavanantham, Sumathi, Eldin, Elsayed Tag
Format: Article
Language:English
Subjects:
Accuracy
Air pollution
Algorithms
Cameras
Classification
Deep learning
Drone aircraft
Drones
Environmental monitoring
Forest & brush fires
Forest fire detection
Forest fires
Geospatial data
Global positioning systems
GPS
Image acquisition
Image quality
India
Land use planning
Machine learning
Remote sensing
Satellite imagery
Satellites
Sensors
Technology application
Unmanned aerial vehicles
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Forest fires are caused naturally by lightning, high atmospheric temperatures, and dryness. Forest fires have ramifications for both climatic conditions and anthropogenic ecosystems. According to various research studies, there has been a noticeable increase in the frequency of forest fires in India. Between 1 January and 31 March 2022, the country had 136,604 fire points. They activated an alerting system that indicates the location of a forest fire detected using MODIS sensor data from NASA Aqua and Terra satellite images. However, the satellite passes the country only twice and sends the information to the state forest departments. The early detection of forest fires is crucial, as once they reach a certain level, it is hard to control them. Compared with the satellite monitoring and detection of fire incidents, video-based fire detection on the ground identifies the fire at a faster rate. Hence, an unmanned aerial vehicle equipped with a GPS and a high-resolution camera can acquire quality images referencing the fire location. Further, deep learning frameworks can be applied to efficiently classify forest fires. In this paper, a cheaper UAV with extended MobileNet deep learning capability is proposed to classify forest fires (97.26%) and share the detection of forest fires and the GPS location with the state forest departments for timely action.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12030733