FOREST FIRE DETECTION

Forest fires represent a considerable danger to ecosystems, wildlife, and human life, resulting in substantial environmental and economic harm annually. Early detection is essential for reducing the spread and effects of such calamities. This project introduces an AI-driven Forest Fire Detection System that employs Convolutional Neural Networks (CNNs) for the automated identification of fire and smoke from images and video streams. The system is developed using Python and is deployed via a Flask-based web application for real-time monitoring and user engagement. The proposed system combines a trained deep learning model with image preprocessing methods to categorize input media as “Fire” or “No Fire.” To improve reliability, a color-based analysis in the HSV color space is integrated to identify fire-like patterns (red, orange, and yellow shades), thereby enhancing detection performance in difficult situations. The system accommodates various input formats, including JPG, PNG, MP4, AVI, and MOV, and processes both still images and video frames. Upon the detection of fire, the system automatically activates alert mechanisms, including email notifications containing detection specifics and optional alarmsounds based on a set confidence threshold. The application features a user-friendly interface for file uploads, viewing detection outcomes, and monitoring real-time analysis. Testing tools and manual trigger options are provided to guarantee system reliability and facilitate evaluation. The proposed solution presents a scalable and cost-efficient method for early forest fire detection and can be integrated with surveillance cameras and remote monitoring systems. With additional optimization and deployment in cloud environments, the system can accommodate large-scale forest monitoring and disaster management initiatives. 2.