Early Fire Detection in Forest Using Image Processing

Abstract

Early fire detection stays vital because forest fires threaten natural ecosystems as well as local wildlife population and nearby residential areas. Fire detection methods that depend on manual patrolling and satellite imaging along with thermal sensors tend to be expensive yet offer slow responses with unpredictable precision levels. The proposed system implements a real-time forest fire detection mechanism through image processing with deep learning technology that combines Raspberry Pi 4B and Pi Camera and aerial drone surveillance. The You Only Look Once (YOLO)v5s object detection model within the system performs accurate early fire signal detection from drone image acquisition. The detection triggers the system to determine exact position information with a Global Positioning System (GPS) module that sends location data to a Firebase web platform. The solution enables automatic alerts to emergency teams. The system demonstrates cost-efficient operation along with scalability features and quick response times which allows it to work continuously in forest areas and high-risk zones. Embedded systems, aerial surveillance, Artificial Intelligence (AI) technology together with real-time reporting within the recommended system design boost forest fire detection as well as emergency response speed.