SF‑YOLO: Lightweight Smoke and Fire Detection for Natural Scenes

2025-07-31

Published in Fire (2025), SF‑YOLO (“Smoke and Fire‑You Only Look Once”) is a lightweight real‑time detection model tailored for natural environments—forests, fields, campgrounds—where fires may spark and spread unpredictably mdpi.com.

Model Design

SF‑YOLO is based on YOLOv11 backbone with a two-path residual attention module (C3k2) and an embedded attention mechanism in the detection head. Its design targets small object detection, occlusion scenarios, and ambiguous flame/smoke boundaries—common challenges in wildland settings. The model emphasizes low computational demand, making it deployable on edge devices like drones or surveillance cameras mdpi.com.

Performance

Compared to conventional methods, SF-YOLO significantly improves detection speed and accuracy in real-world natural scenes. While full metrics weren’t publicly listed, authors report favorable results in smoke/flame identification and low false alarm rates.

Applications

Ideal deployment environments include:

Forest fire surveillance cameras
Drone-based patrols
Protected area monitoring

By detecting early, faint flame or smoke signals and running on low-power hardware, SF‑YOLO becomes a practical tool for early warning.

Analysis

SF‑YOLO meets a critical need: scalable, affordable fire detection in regions lacking infrastructure. Its emphasis on small size, boundary ambiguity, and environmental occlusion shows maturity for real-world deployment. When combined with solar-powered IoT systems, SF‑YOLO can form the basis of an autonomous fire watch network—alerting communities before fires escalate.

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