Fire & Smoke Detection: AI-powered fire and smoke detection, enabling real-time alerts and rapid emergency response
Project Overview
This project developed a fire and smoke detection system using YOLOv5, with a focus on real-time detection for enhanced safety in both residential and commercial spaces. A custom dataset was created by sourcing diverse fire and smoke-related videos from YouTube, ensuring a comprehensive training set. The primary objective was to provide accurate and timely detection to improve safety protocols.
Problem Statement
Early detection of fire and smoke is critical to preventing property damage, injury, and loss of life. Existing detection systems often fail to accurately identify fire and smoke, especially in complex or cluttered environments, leading to delays in response. Additionally, there was a need for a scalable solution that could be deployed across various settings, ranging from residential homes to large industrial sites.
Key Findings
- Robust Dataset Development: Curated a comprehensive dataset covering diverse fire and smoke scenarios to ensure effective model training and high detection accuracy across real-world environments.
- Feature Identification for Hazard Detection: Isolated and utilised critical visual features specific to fire and smoke, enabling the model to achieve greater precision and lower false positives.
- Real-Time Operational Performance: Achieved reliable real-time detection, dramatically outperforming conventional systems in speed and responsiveness during live hazard situations.
Implemented Solution
Trained YOLOv5 on a custom dataset with augmentation techniques, created a seamless deployment pipeline for integration, and implemented an alert system for rapid fire or smoke detection:
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YOLOv5 Model Training:
Trained a YOLOv5 model on the custom dataset with advanced augmentation techniques to increase generalisation and improve performance across various environmental conditions.
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Deployment Pipeline:
Engineered a streamlined deployment pipeline for effortless integration into existing security systems, ensuring the solution could be adopted without disrupting current infrastructure.
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Instant Alert System:
Integrated a real-time alert mechanism that automatically notifies authorities or assigned personnel the moment fire or smoke is detected—supporting swift action and reduced response times.
Results
The AI-driven detection system achieved an impressive 92% accuracy rate in identifying fire and smoke, significantly enhancing early warning capabilities. False positives were greatly reduced, resulting in more actionable alerts for first responders and on-site personnel. The lightweight architecture enabled smooth integration into existing surveillance infrastructure, while the modular design allowed for deployment across a wide range of settings. This solution not only improved real-time safety monitoring but also provided a scalable foundation for future smart surveillance and building safety technologies.