# Real-Time Fire Detection and Alert System
About the Project
The idea for this project was born out of a simple yet urgent question: "How can we leverage AI and technology to detect fire incidents early and prevent major disasters?" With the increasing frequency of fire-related accidents in both urban and rural areas — often due to late detection — we envisioned a smart, real-time system that can monitor live CCTV feeds and instantly notify users when fire is detected. This project aims to empower property owners, institutions, and municipalities to act faster, save lives, and minimize damage.
What Inspired Me
Growing up in a region where emergency services can sometimes be delayed, we’ve witnessed how devastating a fire can be — not only to property, but to communities. This system was inspired by the need to provide a first line of defense using affordable and scalable technology. The inspiration also came from combining the power of AI (YOLOv11) with IoT surveillance systems, and delivering alerts in real-time through mobile technology. Our goal was to create something that’s practical, accessible, and impactful.
How I Built It
Detection Module
- We used YOLOv8, a state-of-the-art object detection model, and fine-tuned it with fire and non-fire datasets.
- Integrated it with live CCTV feeds to scan frames in real-time for signs of fire.
Backend
- Built a Django REST Framework (DRF) API to handle communication between the detection module and the rest of the system.
- The API stores alert logs, manages user/device registration, and sends push notifications when a fire is detected.
Admin Dashboard
- Designed a web dashboard for system administrators to:
- Monitor live system status
- Review alert history and performance analytics
- Manage user accounts and settings
What We Learned
- Gained hands-on experience with computer vision and real-time video processing.
- Learned how to effectively use YOLOv8 for object detection.
- Understood the importance of asynchronous communication between services (model → backend → Dashboard).
- Explored the integration of web socket for alert notifications.
- Improved our knowledge of Django and REST API consumption.
Challenges We Faced
- Model Accuracy: Tuning YOLOv8 to reduce false positives (like smoke from food or lights) was challenging and required extensive dataset curation.
- Real-time Performance: Processing high-resolution video in real-time required optimization and GPU acceleration.
- Notification Timing: Ensuring near-instantaneous notifications from detection to web dashboard was tricky, especially under poor network conditions.
What's Next?
- Add multi-camera support with location-based tagging.
- Integrate automated fire response systems like sprinkler triggers or emergency calls.
- Build an mobile app.
- Extend the model to detect smoke.
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