Real-Time Fire Hazard Monitoring with Deep Learning

Fire outbreaks pose a significant threat to lives and property, making early detection crucial for minimizing damage. Traditional fire detection methods often rely on manual monitoring or conventional image analysis techniques, which can lead to delayed detection and lower accuracy. To address these challenges, this project implements an AI-powered fire detection system using the yolo8 object detection model. The model has been trained on a dataset of 2,509 images, with 1,004 used for training, 754 for validation, and 751 for testing. The system processes video input in real time, detecting fire and marking affected areas with a bounding box and confidence score. Detection details, including the timestamp, fire status, and confidence level, are logged in a CSV file for record-keeping. Additionally, an automated alert system is integrated using Twilio’s SMS service, which immediately notifies designated authorities upon fire detection. The model achieves a mean Average Precision (mAP) of 91.3%, a precision of 90.3%, and a recall of 86.9%, demonstrating high reliability in identifying fire incidents. With its ability to detect fire efficiently and provide real-time alerts, this system offers a scalable and effective solution for fire monitoring and prevention.