A FIRE DETECTION SYSTEM THROUGH NEURAL NETWORK
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Universiti Malaysia Sarawak (UNIMAS)
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Fire detection systems are important in ensuring early identification and response to fire risks, preventing loss of property and life. This project introduces a Fire Detection System powered by advance technologies such as computer vision and neural network to improve detection accuracy and efficiency. The system integrates data from real-time video feeds to identify potential fire risks. By employing a trained neural network model on datasets of fire images, the system can identify early signs related to fire risks such as flames and smoke with high accuracy. This system is ideal for deployment in residential, commercial, and industrial settings because it aims to provide real time alert to prevent sudden appearance of fire and reduce response time. With combination of real time monitoring and intelligent decision making, this system ensures a proactive approach to fire safety. This project’s main contributions are improved detection accuracy, reduced false alarms, and smooth integration with the current safety infrastructure, which makes it an affordable and dependable modern fire management system.