The rapid spread of fires underscores the urgency of high-accuracy fire smoke detection for public safety, but early fires pose major challenges—small flame/smoke targets, blurred boundaries, low contrast, and complex background interference—limiting the performance of existing models. To address these issues, this paper proposes SFFA-YOLO, an engineering-oriented improved algorithm based on the YOLOv11 framework for fire smoke detection, which achieves a balanced trade-off between detection precision, real-time performance, and lightweight deployment. The model integrates three synergistic optimization modules for targeted scene adaptation: (1) the FMFA module for cross-scale feature fusion to enhance thin smoke and small flame recognition; (2) the SGCA module for joint channel-spatial feature focusing to improve target localization accuracy; (3) the SDA-Loss function for dynamic weight adjustment based on target size and clarity to stabilize small target detection. Validated on the self-built FS-Blend dataset (supplemented with difficult samples such as distant thin smoke and backlit flames), SFFA-YOLO outperforms mainstream models (YOLOv8, YOLOv9, Faster R-CNN) in key metrics. Compared with the YOLOv11s baseline, it achieves a 2.5% Precision improvement and 3.9% mAP@0.5 improvement while reducing parameters by 12.8%, confirming its reliability as a real-time fire smoke detection solution.
Jiao et al. (2026) studied this question.