Using Convolutional Neural Networks and Deep Learning Models for Forest Fire Detection and Protection

Abstract

Devastating natural catastrophes of unfathomable scale are caused annually by hundreds of forest fires throughout the world. A plethora of well researched options are either waiting to be tested or are already in use to address this issue. To find out where the fire is, people are using sensors. However, extensive forest areas do not meet this condition. Using state-of-the-art technology, we provide a novel method for fire detection in this article. More specifically, we suggested an AI platform. Recognition and detection of smoke and fire using computer vision echnologies, fed by still photos or video data from cameras. One way to determine the fire's intensity is via a "convolution neural network," a deep learning technique. Because of this, forest video surveillance systems will be able to deal with more complicated real-life circumstances. The accuracy is determined by the datasets, the method we will employ, and the separation of the datasets into a train set and a test set.