Flame Guard CNN-Based Wildfire Detection

Wildfires are a serious hazard to the environment, wildlife, and human habitation, which requires prompt detection and action. Conventional fire detection is expensive, time-consuming, and inefficient in preventing large-scale damage through satellite observation and manual surveillance. To overcome these limitations, this study introduces Flame Guard, a deep learning-powered wildfire detection system based on YOLOv8 for real-time fire detection from CCTV and uploaded videos. The system processes live video feeds from CCTV cameras, identifies fire with more than 80% confidence, and automatically sends an email notification with the location of the camera and a snapshot of the identified fire. Moreover, users can manually upload videos for processing in which the model scans frames and provides a CSV output with major attributes such as frame number, timestamp, classification of fire, and confidence. The system further uses a MySQL database to hold history fire events so that users can view past fire data with a download facility for analysis. Integration of computer vision, real-time monitoring, and automated notifications streamlines the process of detecting wildfires, decreasing response time and possible damage. This research illustrates how deep learning and computer vision can be used to efficiently prevent wildfires and protect the environment, thus it is an efficient tool for forest management and emergency response units.