Deep Learning Applied to Forest Fire Detection - Arteaga, Byron; Diaz, Mauricio; Jojoa, Mario (IEEE International Symposium on Signal Processing and Information Technology, 2020)
Nowadays, fires in forest areas are very frequent, mainly caused by climate change and bad practices by the people who live in these areas. In the world the climatic "El Niño" phenomenon has intensified in recent years, increasing the frequency of forest fires, due to high temperatures and prolonged periods of drought that occur. Most forest fires are detected visually and from the ground or from the air using a helicopter; this method is not very efficient since it takes too long to alert the relief corps and requires well-organized logistics. The lack of early detection means has been evident in the events that have occurred in recent months (last fires) and it can be concluded that there are not enough measures to counteract this problem.The purpose of this article is to evaluate the performance of different CNN models pre-trained in the classification of forest fire images, which can be applied in economic development cards such as a Raspberry.