Fire Detection And Extinguishing System

Abstract

Advanced detection and suppression systems are imperative for early intervention in various environments where fire incidents pose significant risks. A comprehensive fire detection and extinguishing system that uses computer vision and image processing techniques to address important fire safety issues is presented in this project. Using an SSD-MobileNet-v2-FPNLite object detection model to analyze video feeds, the system uses a webcam to detect fires in real-time. The system acts quickly minimizes damage and possible loss by automatically initiating a suppression mechanism upon detecting fire. With automated detection, suppression, and improved situational awareness, integrating advanced technologies facilitates proactive fire management. To ensure the system's usefulness, effectiveness and feasibility analysis considers technical, operational, and financial factors. An incremental development model is used, with a focus on feedback integration and continuous improvement. This emphasizes continuous improvement through the integration of feedback. Metrics from the performance evaluation show how well the system detects fire incidents and launches suppression actions promptly, on time. This offers a significant advancement in automated fire management by demonstrating the potential of using cutting-edge image processing and machine learning techniques to address fire safety issues. This system is a significant advancement in fire safety technology that may shorten response times and lessen the effects of fires in a various context.