Firo: Predict, Prevent, Protect

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  Firo Dashboard

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  Prediction Analytics

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  Dashboard while fire outbreak

💡Inspiration

By 2040, over 70% of the world’s forests are projected to face irreversible damage due to wildfires. This alarming statistic among others fueled our mission to create a cutting-edge solution that shifts the narrative from reacting to preventing.

⚙️What it does

Our project is a revolutionary wildfire prevention and monitoring system that combines cutting-edge AI with real-time data to stop fires before they spread. Through predictive analysis, live monitoring, object detection, and temperature sensors, we offer a proactive approach to wildfire management.

The competitive advantages of our approach are:

• Proactive Prevention: Unlike traditional systems that react to fires, our solution predicts and prevents them, minimizing damage.
• AI-Driven Accuracy: Advanced machine learning ensures precise forecasts, even in complex environments.
• Comprehensive Data Integration: Merges environmental data like temperature, humidity, and wind speed for insights.

🛠️How we built it

1. YOLO v9 (Python): We leverage the YOLO v9 object detection algorithm, to ensure rapid and precise identification of potential fire hazards. Its advanced deep learning architecture processes real-time footage from our cameras, detecting flames, smoke, or suspicious heat sources with over 92% accuracy. By training YOLO v9 on a custom dataset of over 3,000 curated images featuring various fire scenarios, we have fine-tuned it to distinguish between actual threats and false alarms, even in complex forest environments.

2. LangChain (Python) for Backend Intelligence: LangChain powers the system’s backend intelligence, enabling seamless integration between predictive models and live data feeds. It connects prediction analysis with real-time monitoring. LangChain also supports decision-making by generating actionable insights, such as calculating the fire's potential spread or providing strategies for first responders.

3. Streamlit (Python, JS) for User Interface (UI): Streamlit provides a clear dashboard displaying live monitoring feeds, predictive analytics, and historical data visualizations.

🪜Challenges we ran into

Key challenges included curating a diverse dataset of over 3,000 fire-related images for robust YOLO v9 training and integrating Python-based machine learning with JavaScript in Streamlit for a responsive user interface.

🎉Accomplishments that we're proud of

Our key accomplishments include achieving over 92% accuracy in detecting fires, even from distant smoke, using our custom-trained YOLO v9 model. Additionally, we've developed a highly responsive predictive model that provides real-time fire risk analysis with minimal latency, ensuring timely alerts.

✒️What we learned

From this project, we learned the importance of combining cutting-edge AI models, such as YOLO, with real-time data collection to create an effective fire detection and prevention system.

We also learned how to train a dataset most effectively by using Google Colab. Its cloud-based environment allowed us to efficiently handle large datasets and perform model training with GPU acceleration. By fine-tuning parameters, we were able to optimize our training process and achieve a high level of accuracy in fire detection.

📲What's next for Firo

Here’s what we plan for the future:

1. We aim to enrich the model with satellite data, providing a more comprehensive view of fire-prone areas, and improving detection accuracy across larger regions.

2. By creating a crowdsourcing platform, we enable citizens to act as real-time fire spotters. Users will be able to report fires and evacuation routes through a network of thousands of cameras and AI-powered social media monitoring.

3. We plan to incorporate human element classification into the system, aiming to detect potential arsonists at the scene. This will be achieved by analyzing patterns in the area and linking fire outbreaks with other suspicious behaviors.

4. We will further improve our datasets and the prediction models.

5. We will expand the app's notification systems to offer more diverse alert methods, ensuring rapid communication with all stakeholders.

Built With

• javascript
• langchain
• python
• streamlit
• yolo