Enhanced fire detection: a weighted bounding box fusion approach based on Dempster–Shafer theory - Choutri,Kheireddine; Hamda,Nour El Imane; Hadjali,Allel; Lagha,Mohand; Meshoul,Souham; Fadloun,Samiha (PeerJ Computer Science, 2026)
In this study, we present an advanced fire detection approach using an improved weighted bounding box fusion technique based on Dempster–Shafer theory. A key challenge in fire detection is the inherent uncertainty in distinguishing true fire events from false positives (e.g., fire-like objects or background noise). Our approach addresses this by fusing overlapping bounding boxes from a You Only Look Once (YOLO)-based detector, explicitly modeling conflicts between fire and non-fire classifications. Dempster–Shafer theory plays a critical role in quantifying uncertainty and reconciling contradictory evidence, enabling more robust decision-making. We further introduce a confidence-aware weighting scheme to prioritize highly reliable detections during fusion. On a diverse fire image dataset, YOLOv5 combined with our Dempster–Shafer Weighted Boxes Fusion (DS-WBF) method achieved a precision of 0.814, recall of 0.863, AP0.5 of 0.875, and AP0.5–0.95 of 0.542, representing gains of +1.24%, +1.17%, +2.46%, and +4.63% over traditional Non-Maximum Suppression (NMS). These improvements reduce false alarms and enhance true positive rates, enabling more reliable early fire detection for real-time monitoring systems.
Keywords: Fire detection; Deep learning; Computer vision; YOLO; Data fusion; Bounding boxes; Dampster–Shafer theory