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FireSentinel - AI Fire Detection
Every second counts - turning cameras into vigilant firefighters
The Cost of Delay
Traditional smoke alarms detect fires too late, often after significant damage has occurred. While surveillance cameras may capture early signs, round-the-clock human monitoring is impractical. This results in critical delays, costly false alarms, and devastating losses of life and property.
$50B+
Annual Damage
globally from fires
15
Avg. Response Time
minutes for fire departments
350K
US House Fires
annually cause devastation
The Solution: FireSentinel
FireSentinel leverages advanced AlexNet-based computer vision and deep learning to accurately detect early signs of fire and smoke. It delivers immediate, verified alerts, acting as a crucial first line of defense before fires escalate. FireSentinel can be deployed as a standalone device or integrated as software into existing CCTV systems.
01
Visual Monitoring with AlexNet AI
02
Instant Fire & Smoke Detection
03
Automated, Verified Emergency Alerts
04
Minimize False Alarms
The Solution: FireSentinel
FireSentinel leverages advanced AlexNet-based computer vision to accurately detect early signs of fire and smoke. This system provides immediate emergency alerts, significantly reducing response times and minimizing damage.
Deployment Forms:
  • Standalone device for comprehensive fire monitoring in new installations.
  • Software integration for existing CCTV and surveillance systems, transforming current infrastructure into intelligent fire detectors.
Investment Opportunity
Seeking $1M pre-seed to accelerate MVP development and pilot deployments.
40%
R&D and AI development
30%
Manufacturing and production
20%
Marketing and partnerships
10%
Operations and team
Traction: Currently developing prototype with AlexNet algorithms. Next 6 months: pilot deployments in wildfire communities. 12+ months: strategic partnerships with fire departments and insurers.
Strong competitive advantages: Superior speed vs satellites, massive scalability vs traditional sensors, continuous AI learning, insurance premium benefits.
Roadmap & Call to Action
01
Phase 1: Prototype Training & Testing
(completed)
02
Phase 2: Pilot Partnerships
With municipal fire departments
03
Phase 3: Commercial Rollout
With IoT and camera manufacturers
Our Ask:
Seeking funding/partnerships to expand model accuracy, integrate alert APIs, and scale production.
Together, we can stop fires before they spread. 🔥
Security and Reliability
Data Security
Encrypted communication (TLS/SSL) for all video and alert traffic
Privacy
On-device inference ensures raw video never leaves the camera (for edge mode)
Fail-Safe Design
  • Offline buffer for frames during network loss
  • Redundant alert relays (SMS, cellular backup)
  • Health monitoring with self-diagnostics and auto-restart
Regulatory Readiness
Designed to comply with NFPA 72 (fire alarm standards) and GDPR for data handling
Scalability and Integration
Integration Interfaces
RTSP, ONVIF, HTTP streams, and SDKs for camera vendors
Cloud Management Console
  • View detection history and camera health
  • Configure thresholds remotely
  • Aggregated analytics (heatmaps, false alarm trends)
Edge + Cloud Hybrid Model
Combines on-device detection with cloud-based retraining
Horizontal Scalability
Supports dozens to hundreds of camera nodes via containerized microservices
Real-Time Inference and Alert System
Latency
<500 ms per frame (224×224 resolution) on standard GPU or Jetson Nano
Continuous Monitoring
Processes frames at ~20 FPS per camera feed
Alert Channels
  • Direct API to fire dispatch
  • Mobile push notifications and email
  • Optional audible/visual alarm module
Safety Thresholds
Tuned to favor high recall (≥95%) to minimize missed detections
Training and Model Optimization
Our AI model undergoes rigorous training and continuous optimization to ensure maximum accuracy and reliability in fire detection.
Dataset:
Thousands of labeled images across varied environments (industrial, urban, forest)
Transfer Learning:
Started from pretrained AlexNet (ImageNet) weights, fine-tuned on fire datasets
Optimization Techniques:
  • Focal loss for imbalanced data
  • Weighted sampling and augmentations
  • Cosine learning rate schedule with AdamW optimizer
Performance Metrics:
98%
Accuracy
0.985
AUROC
<5%
False alarm rate
System Architecture
Input Layer: Real-time video streams from surveillance or IoT cameras
01
Frame extraction and preprocessing
02
Fire detection using AlexNet CNN
03
Confidence scoring and temporal smoothing
04
Alert trigger system with API/SMS dispatch
Deployment Options:
  • Edge Device (embedded AI chip/Raspberry Pi)
  • Cloud API (centralized CCTV systems)
Data Logging:
Detection events stored with timestamp, location, and frame snippet
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