Design and Development of Fire Detection Using Deep Learning Algorithm

Fire detection is a critical aspect of ensuring safety in various environments, including residential, industrial, and commercial settings. In this project, we present the development and the implementation of an IoT based fire detection systems that integrates image capture capabilities with a machine learning algorithm. The system employs an IoT camera to record images of its surroundings, which are subsequently analyzed by a convolutional neural network (CNN) specifically designed for identifying instances of fire. The CNN model analyzes the images in real-time, identifying potential fire hazards based on visual cues. Upon fire detection, the system generates an alert, enabling prompt response and mitigation measures. The integration of IoT technology with machine learning algorithms enables remote monitoring and early detection of fire incidents, enhancing safety and minimizing potential damage. The experimental findings validate the efficacy and dependability of the proposed system in precisely identifying instances of fire. This project contributes to the advancement of fire detection technologies, offering a scalable and efficient solution for enhancing safety in diverse environments..