Wildfire Segmentation using Deep-RegSeg Semantic Segmentation Architecture

Abstract

Wildfires are a worldwide natural risk, which causes harmful effects to human safety and leads to ecological and economical damage. Various fire detection systems have been proposed in order to detect fire and reduce its effects. However, they are still limited in detecting small fire areas and determining the precise fire’s shape. In order to overcome these limitations, we present, in this paper, a novel method based on deep learning, called ‘Deep-RegSeg’, to segment fire pixels and detect fire areas in complex non-structured environments. Deep-RegSeg is evaluated with varying backbone and loss function. The obtained results showed a high performance and outperformed some recent state-of-the-art techniques. The results also proved that Deep-RegSeg is efficient in segmenting wildfire pixels and detecting the precise fire’s shape, especially small fire areas under various conditions of weather, presence of smoke, and environment brightness.