FIRE DETECTION SYSTEM

An intelligent forest fire detection system is developed using YOLOv8 to accurately identify fire and smoke in real time. The model delivers high-speed inference and integrates automated alert mechanisms,makingit suitableforearlywildfireprevention. I. Introduction Forest fires are one of the most critical environmental hazards, leading to widespread destruction of natural habitats, loss of biodiversity, and significant economic damage. Early detection plays a vital role in preventing minor fire incidents from escalating into large-scale disasters. Conventional fire detection methods such as satellite imaging, thermal sensors, and human surveillance towers often suffer from challenges like low resolution, delayed detection, limited coverage, and high operational costs. With the rapid growth of artificial intelligence and deep learning, computer vision–based fire detection systems have emerged as a powerful alternative capable of providing fast and accurate results. By integrating YOLOv8 with backend processing, web technologies, and alert mechanisms, the proposed system aims to provide a cost- effective, scalable, and reliable early-warning solution for forest departments and disaster management agencies. This model-driven approach significantly enhances traditional monitoring systems by offering real-time responsiveness, robust performance, and improved detection accuracy under varying environmental conditions.