Multispectral deep learning models for wildfire detection

Abstract

Aided by wind, all it takes is one ember and few minutes to create wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision based techniques have been proposed for early detection of forest fire using video surveillance. Several computer vision based methods have been proposed to predict and detect forest fires at various spectrum, namely, RGB, HSV and YCbCr. The aim of this paper is to propose multi-spectral deep learning model that combine information from different spectrum at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available dataset is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 1.9% and 14.88% in test and challenge set over those based on single spectrum for fire detection even in challenging environments.