Real-Time Fire Surveillance with Machine Learning Twilio Integration

Abstract

Fire threats pose significant risks to people and property, necessitating efficient surveillance systems. A complete fire surveillance system is proposed using machine learning and artificial intelligence to monitor fire incidents in real time. The system is built using React.js for frontend development and MongoDB for backend storage. Node.js is integrated for server-side operations, ensuring data management and user interaction. The system sends alerts via WhatsApp when image analysis identifies a fire concern, leveraging Twilio for seamless messaging. Robo Flow simplifies computer vision model management, while YOLOv8, a cutting-edge object recognition algorithm, enhances detection speed and accuracy. YOLOv8 is widely used in real-time object identification applications like robotics, autonomous cars and surveillance systems. Twilio is a cloud communication platform that allows developers to integrate voice, video, and SMS into their apps, enabling notifications, alarms, and two-way communication. The research utilizes Machine Learning, React.js, MongoDB, YOLOv8, and Twilio to offer efficient real-time fire surveillance.