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💡 Learning + Project Idea
💡 Learning + Project Idea
Fire accidents often escalate due to delayed human detection and response.
This project aims to automate the detection and initial alert process by combining deep learning with a simple web interface:
Live video stream is analyzed in real time.
YOLOv8 identifies potential fire regions within frames.
Once detected, the Flask app updates the status and triggers an audio alarm.
The system can be further extended to activate automated sprinklers or alert systems in smart buildings.
This makes it suitable for environments like factories, warehouses, and laboratories where early fire detection is critical.
💡 Project Idea –
💡 Project Idea –
🔥 Automated Fire Detection and Response System
🔥 Automated Fire Detection and Response System
🧠 What I Learned
🧠 What I Learned
Working on this project helped me understand:
How to use YOLOv8 for object detection tasks such as fire recognition.
Integrating deep learning models with Flask web applications.
Handling real-time video streams and rendering annotated frames.
Creating lightweight APIs for continuous detection and user interface updates.
Building responsive alert mechanisms to enhance system safety.
This project strengthened my practical understanding of AI deployment, edge computing, and real-time event handling.
⚙️ Technical Details
⚙️ Technical Details
Framework: Flask (Python backend for video streaming and API)
Model: YOLOv8 custom-trained fire detection weights (best.pt)
Libraries: OpenCV, Torch, Ultralytics YOLO, NumPy
Alert Mechanism: Audio alarm (alarm.mp3) triggered on fire detection
Frontend: HTML-based control UI for start/stop and status display
🎯 Project Objective
🎯 Project Objective
The objective of this project is to develop an AI-powered, real-time fire detection and response system that can automatically identify fire incidents through video streams and trigger immediate alerts.
This system uses a YOLOv8 deep learning model integrated with a Flask web application to:
Continuously monitor live camera feeds for fire occurrences.
Detect and highlight fire regions in real time using computer vision.
Instantly activate an audio alarm to alert nearby individuals.
Provide a simple and accessible web interface for live monitoring and control.
The ultimate goal is to reduce human dependency and response time in emergency situations by creating an automated, intelligent safety system that can be integrated into buildings, factories, and public spaces.
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