EdgeFireSmoke plus plus : A novel lightweight algorithm for real-time forest fire detection and visualization using internet of things-human machine interface

Forest fires can have severe impacts on both the environment and human communities. They can cause soil erosion, loss of habitat and biodiversity, as well as the release of carbon dioxide and other pollutants into the atmosphere. In addition, they can cause damage to properties, displacement of residents, and put firefighters and other responders at risk. Forest fires can also contribute to climate change by releasing stored carbon into the atmosphere and altering ecosystems. In this work, we propose a novel algorithm capable of monitoring small areas of forest reserve environment through video streaming in real-time. It will complement the existing means of forest monitoring and surveillance and provide effective solutions faced in satellite-based monitoring. The proposed algorithm is an improvement of the EdgeFireSmoke method and uses an artificial neural network together with a deep learning method. The proposed EdgeFireSmoke++ algorithm was able to detect forest fires with 95.41% accuracy and 95.49% accuracy from the evaluated dataset. The real-time experiment performed very well for use with Internet Protocol cameras, reaching 33 frames per second. This test was superior concerning the evaluated methods in the literature.