Real Time Video Fire Smoke Detection Using ML

Fire outbreaks are a significant threat to life and property, necessitating the need for efficient and reliable detection systems. This project proposes a real-time fire and smoke detection system utilizing machine learning (ML) and the Internet of Things (IoT). The system leverages video feeds from IoT-enabled cameras to monitor environments and detect fire and smoke occurrences with high accuracy. By employing advanced machine learning algorithms trained on diverse datasets of fire and smoke imagery, system ensures robust detection while minimizing false alarms. The IoT integration allows real-time data transmission to centralized monitoring platforms, enabling immediate alerts and prompt actions. Additionally, the system’s scalability ensures it can be deployed across various environments, including industrial, residential, and forested areas.