Fire & Smoke Detection

The Problem

Traditional fire detection relies on smoke and heat sensors with limited coverage areas, delayed activation, and high false alarm rates in dusty or steamy environments. In large open spaces — warehouses, outdoor storage, forests — sensor-based detection is often too slow or impractical. Seconds of earlier detection can prevent a manageable fire from becoming catastrophic.

How Computer Vision Solves It

Computer vision detects visible smoke and flame from camera feeds, providing visual fire detection across large areas where point sensors are impractical. Detection occurs at the first visible sign of smoke or flame — often 30–60 seconds before conventional sensors activate — and includes location context from the camera view.

The Challenge with Current Solutions

Smoke appearance varies dramatically — thin wisps, dense plumes, white, gray, black. Steam, dust, fog, and exhaust can resemble smoke visually. Fire detection must work across day and night conditions. False alarms in industrial environments with legitimate heat and vapor sources undermine trust.

What vfrog Brings That's Different

vfrog trains fire and smoke models on your specific environment — distinguishing process steam from smoke, exhaust from fire. Synthetic data covers fire progression stages and smoke behaviors under different conditions. Site-specific calibration minimizes false alarms from known vapor and dust sources.

Key Benefits

• Earlier detection than traditional smoke sensors
• Large area coverage from existing cameras
• Location context for faster response
• Reduced false alarms through environment-specific training
• Day and night operation
• Integration with fire alarm and suppression systems