CNN-Based Real-Time Forest Fire Detection: A Cost-Effective Solution

Abstract

Wild fires are for real a menace to the forests, people and the animals they impact by burning the homes of people and the natural habitats of the animals. Satisfying the identified gap pertaining to the lack of effective solutions for the early identification of fire and, eventually, for intervention in the process of rung fires, this investigation suggests the development of a forest fire detection system that would involve Convolutional Neural Networks (CNNs), controlled through a laptop’s inbuilt Webcam and authentic image processing. This is because methods like fire watchtowers and satellite images are costly, cover a smaller area and is not as prompt as our CNN-based system that trains a CNN to detect fire by identifying peculiarities in scenes comprising of forests. This system proposed to capture images from a webcam in real-time manner and the CNN part of the system is evaluating the images for the fire presence. It raises a sharp alarm both audible and visible as soon as the fire is detected so that response may be initiated as was necessary to put out forest fires. The CNN architecture is fabricated based on the Kaggle data set through training on this data set from the high accuracy point of view for confirming the near 99% accuracy for detecting the forest fire with the help of the convolution neural network that improves the possibility compared to the traditional way. For this reason, a high accuracy would always help in avoiding situations wherein real fires are confused with false alarms, whereas, learning in the model helps avoid some fires being detected or alarmed when they are not, hence act as the building blocks to improving the overall performance of the AI algorithm. This is the case with the system that accesses regular hardware for example the laptops and webcams; this makes them easy to deploy most especially in the developing world.