UAV Based Forest Fires Detection Using Edge Computing and IoT

Abstract

Forest fires are ravaging many ecosystems across the world. Most of the fires start  from  a  single  point  and  grow to immense proportions, making them extremely difficult  to deal with. This paper defines a rapid response system that   can be deployed in forested areas in order  to  provide  quick  and appropriate response to forest fires, before they spread over large areas. The proposed system uses a UAV that is equipped with a camera and a  microprocessor,  like  a  Raspberry  Pi.  The microprocessor processes the camera feed to identify the presence of fire using a convolutional neural network, either by utilizing on-board resources or using supplementary resources made available to it in the form of a Vision Processing Unit (VPU) plugged into its USB port. The benchmarks associated with one such neural network running on the Intel Neural Compute Stick 2 is also supplied. In the event of a fire being detected, the microprocessor issues a command to the actuators, like speakers and sprinklers that are deployed on the ground at appropriate intervals, thus activating them in an effort to ward off animals and reduce the loss of wildlife.