EdgeFireSmoke: A Novel Lightweight CNN Model for Real-Time Video Fire-Smoke Detection

The planet Earth is being affected by a series of wildfires, which have been steadily increasing over the last two decades. Forests have undergone deforestation due to natural forest fires and forest fires caused by man. These events are occurring on a global scale, and in Brazil, these wildfires are having an extreme impact on the Amazon forest as well as other forest biomes. This article proposes a novel lightweight convolutional neural network (CNN) model for wildfire detection through RGB images. This new method presents more advantages than the other methods used for the same task. Our CNN architecture can be used with aerial images from unmanned aerial vehicles and from video surveillance systems, combined with edge computing devices for image processing with a CNN. The proposed system is able to send wildfire alerts. The images do not have to be sent to a cloud computer as they can be processed in an edge device. However, it sends a string of alerts whenever a wildfire is detected. The evaluation of our proposed method showed that it required about 30 ms for the classification time, per image, and achieved an accuracy of 98.97% and an Fl-score of 95.77%, which is a very promising result.

Automated summary

This article proposes a novel lightweight convolutional neural network (CNN) model for wildfire detection through RGB images. The proposed method shows advantages in efficiency and accuracy, and can be combined with unmanned aerial vehicles and video surveillance systems for image processing. The ability to send timely wildfire alerts makes this method significant for forest protection.