ARIES

Fire detection today is still dominated by traditional smoke and heat sensors that react only after the situation becomes dangerous. They also provide no understanding of what type of fire is happening, how severe it is, or whether people nearby are safe. At the same time, computer-vision-based fire detectors that exist often trigger false positives because they cannot differentiate between harmless flames and actual hazards. This makes real-world deployment difficult.

ARIES (Advanced Real-Time Intelligence & Emergency System) aims to solve this gap by using computer vision to provide early, accurate, and context-aware fire detection. Instead of depending on specialized sensors, ARIES analyzes live video from any camera—CCTV, webcams, mobile phones—and identifies fire, smoke, and hazardous flame behavior in real time. The main goal is to make computer-vision-based fire detection practical by reducing false positives and introducing severity-based logic.

1. Core Idea

The idea behind ARIES is simple: use the cameras that already exist in buildings and infrastructure, and upgrade them with an intelligent, multi-model computer vision system that understands what kind of fire is happening and how dangerous it is. Unlike CV systems that treat all fire the same, ARIES distinguishes between:

• Contained fire (low risk)

• Hazardous fire (HZRD) with violent flames and heavy smoke

• Smoke-only early warnings

This makes the system more reliable and significantly reduces unnecessary alarms.

2. How the System Works

ARIES uses a multi-model inference pipeline built on YOLOv8, optimized with a custom “gating” logic. The models include:

1. smokenfire.pt: A lightweight model that detects normal fire and smoke.

2. HZRD_fire_detection_best.pt: A custom-trained model focused on detecting high-risk flame behavior.

3. PPE.pt: Activated only when a hazard is present, used to detect firefighters and unprotected workers.

This design ensures the system stays fast and avoids running heavy models unnecessarily.

Logic Flow

1. Frame Capture
   Live video frames are captured using the browser (webcam or uploaded video).

2. Initial Detection
   The lightweight model checks for fire and smoke.

3. Severity Analysis (Gating)
   If fire or smoke is present, ARIES activates the HZRD model to check if the fire is dangerous or just contained.

4. Human-Hazard Correlation
   If a hazardous fire is detected, ARIES activates the PPE model to see if:

   • People are nearby

   • They are wearing protective gear

   • A firefighter or response unit has arrived

5. Result Packaging
   The backend returns bounding boxes, detected classes, severity alerts, and the percentage of the frame covered by HZRD/smoke.

6. Frontend Visualization
   The UI overlays bounding boxes, displays hazard popups, and logs timestamps for every detection.

3. Real-Time Processing and Visualization

The frontend is built using HTML, JavaScript, and Canvas, ensuring low-latency rendering without heavy frameworks.
The browser sends frames to the FastAPI backend, which performs inference and sends back structured results.

The UI shows:

• Live video stream

• Processed output with bounding boxes

• HZRD/smoke alerts

• Area coverage

• A real-time detection log panel

This design keeps the system responsive even when running on CPU-only environments, such as Hugging Face Spaces.

4. Overall Benefits

ARIES solves multiple real-world problems:

• Faster detection compared to smoke/heat sensors

• Lower false positives compared to standard CV fire detectors

• Understands severity, not just presence of fire

• Identifies if people are nearby and whether they are protected

• Works with existing camera infrastructure, reducing costs

• Edge-ready, capable of running on CPUs, embedded devices, and industrial hardware

• Provides actionable insights rather than simple alarms

The system is particularly useful in:

• Warehouses

• Industrial plants

• Chemical storage sites

• Smart city infrastructure

• Construction sites

• Public safety facilities