Pyrowatch
From first spark to first response
Inspiration
Wildfires are unpredictable and fast-spreading, causing billions in damages yearly. Traditional detection methods rely on satellite imaging or thermal sensors—too slow and too expensive. Pyrowatch is a real-time, vision-based AI that makes wildfire detection accessible and immediate.
What it does
Pyrowatch transforms a Raspberry Pi and a camera into an AI-powered fire detection system that:
- Recognizes early fire patterns through real-time image analysis
- Identifies fire before it escalates
- Works in varied lighting and weather conditions
- Operates without thermal sensors or external data
- Provides instant alerts to responders for faster action
How we built it
- Trained a deep learning model on thousands of fire images
- Optimized AI for low-power Raspberry Pi hardware
- Developed a networked detection system for wide coverage
- Integrated an instant alert and monitoring dashboard
- Tested for reliability in real-world environments
Challenges we ran into
- Running AI on limited Raspberry Pi resources
- Ensuring accurate fire recognition in different landscapes
- Handling low-light and high-glare conditions
- Reducing false positives while maintaining sensitivity
?? Accomplishments we're proud of
- 1% the cost of traditional fire detection systems
- Real-time AI vision with faster-than-satellite response
- Successfully tested in diverse environments
- Developed an AI that adapts to changing fire behaviors
What we learned
- Vision-based AI can revolutionize early fire detection
- Edge AI must be lightweight yet powerful
- Wildfire detection needs fast and precise decision-making
- Collaboration with emergency teams is key for impact
What's next for Pyrowatch
- Expanding to wildfire-prone regions
- Enhancing AI for nighttime fire detection
- Developing solar-powered, self-sustaining units
- Growing an open-source community for innovation
- Exploring industrial and urban fire detection applications
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