logo
Shenzhen Knowhow Technology Co.,limited
english
français
Deutsch
Italiano
Русский
Español
português
Nederlandse
ελληνικά
日本語
한국
quote
english
français
Deutsch
Italiano
Русский
Español
português
Nederlandse
ελληνικά
日本語
한국
Home
about us
Company Profile
factory tour
quality control
FAQ
products

Laser Fume Extractor

Solder Fume Extractor

Fume Extractor For DTF Printer

Beauty Salon Fume Extractor

Dental Fume Extractor

Lab Fume Extractor

Welding Fume Extractor

Moxibustion Fume Extractor

Mobile Fume Extractor

Portable Fume Extractor

Fume Extractor Accessories

Industrial Dust Collection Equipment

Electronic Tools

Automatic Tape Dispenser

Automatic Screw Feeder

Automatic Label Dispenser

Solder Rework Tools

Soldering Accessories

Electronics Test Instruments
blogs
solutions
contact us
quote
home
about us
Company Profile
factory tour
quality control
FAQ
products

Laser Fume Extractor

Solder Fume Extractor

Fume Extractor For DTF Printer

Beauty Salon Fume Extractor

Dental Fume Extractor

Lab Fume Extractor

Welding Fume Extractor

Moxibustion Fume Extractor

Mobile Fume Extractor

Portable Fume Extractor

Fume Extractor Accessories

Industrial Dust Collection Equipment

Electronic Tools

Automatic Tape Dispenser

Automatic Screw Feeder

Automatic Label Dispenser

Solder Rework Tools

Soldering Accessories

Electronics Test Instruments
blogs
solutions
contact us
quote
banner
Blog Details
Created with Pixso. Home Created with Pixso. Blog Created with Pixso.

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

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.

banner
Blog Details
Created with Pixso. Home Created with Pixso. Blog Created with Pixso.

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

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.