Fire Situation Monitoring Through Subtractive Counterfactual-Based Visual Segmentation

Fire situation monitoring is critical for protecting lives and property, offering real-time data on fire location, intensity, and progression. Traditional vision-based methods often treat fires as rigid objects, overlooking their fuzzy boundaries. To address this, we propose the subtractive counterfactual-based fire segmentation (SCF) method, which enhances edge detail focus for precise fire profiling. The approach constructs a counterfactual image by masking flames. Factual and counterfactual features are extracted and hypothetical counterfactual features are introduced during inference. Subtracting factual features from hypothetical counterfactual features yields unbiased fire features for segmentation. Experiments on five datasets demonstrate SCF's superior accuracy and reliability in fire monitoring.