Fire Detection Using Unmanned Aerial Vehicle

Mays Mohammed Nabeel; Shaymaa W. Al-Shammari

doi:10.58564/IJSER.2.1.2023.60

Authors

Mays Mohammed Nabeel Department of Computer Engineering, College of Engineering, AL-Nahrain University, Baghdad, Iraq
Shaymaa W. Al-Shammari Department of Computer Engineering, College of Engineering, AL-Nahrain University, Baghdad, Iraq

DOI:

https://doi.org/10.58564/IJSER.2.1.2023.60

Keywords:

UAV, Fire detection, deep learning, Pixhawk, and Raspberry-Pi4

Abstract

Fire is one of the most dangerous types of accidents that can occur and may cause huge harm to people's lives and threaten environmental resources by spreading quickly, which makes it out of control. This critical aspect requires an intelligent system to control it correctly and as soon as possible. To achieve this, a platform has been proposed for a fully automated fire detection system to overcome fire spreading, this goal has been achieved by utilizing the UAVs (Unmanned Aerial Vehicles) as an IoT service role equipped with Pixhawk4 as a flight controller and an onboard fire detection system using Raspberry-Pi4 equipped with a camera to detect the fires using Convolutional Neural Network (CNN) algorithm. The drone systems' particular properties of high speed, flexible movement, and easy control, as well as the embedded sensors in flight controller like gyroscope, accelerometer, and compass, make it the best choice for the real-time monitoring system. The whole system was tested and the real-time fire video was detected using the MobileNetv2 as a CNN pre-trained model which has trained and achieved an accuracy 99.79% .

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