Fire Detection Using Deep Learning

Fire accidents can cause major damage to human life, property, industries, and the environment. Traditional fire detection systems such as smoke sensors and heat detectors are commonly used, but they often suffer from delayed response time, limited coverage, and false alarms. In large environments, manual monitoring through CCTV cameras is also difficult and requires continuous human attention. To overcome these limitations, this project proposes an intelligent fire detection system using Deep Learning and Computer Vision techniques. The system uses a Convolutional Neural Network (CNN) model to detect fire from images and real-time video streams. Video input is captured through a webcam or IP camera, and each frame is processed using OpenCV techniques. The CNN model analyzes important visual features such as flame color, texture, brightness, and motion patterns to classify the frame as “Fire” or “No Fire”. Whenever fire is detected, the system immediately generates warning alerts and stores the detection details with date and time in the database. A Flask-based web dashboard is also developed for real-time monitoring and viewing detection logs. The proposed system helps in early fire detection, reduces false alarms, minimizes manual effort, and improves overall safety. Experimental results show that the system provides accurate and fast fire detection in real-time environments. The proposed solution can be used in industries, residential buildings, forests, public places, and smart surveillance systems for effective fire safety management.