Drone-Based Fire and Smoke Detection System

This project aims to develop a drone-based fire and smoke detection system utilizing the YOLOv8 deep learning model, retrained on the extensive Flame and Smoke Detection Dataset (FASDD). The system will enhance real-time detection of fire incidents across diverse environments, leveraging the dataset's rich variety of fire scenarios. Equipped with 2G/3G/4G communication technologies, the drone will transmit live data and alerts to monitoring stations, ensuring immediate response capabilities. This connectivity is crucial for effective fire management in remote or hard-to-access areas, allowing for timely interventions. By integrating YOLOv8, recognized for its high accuracy and efficiency in object detection, the system will effectively differentiate between fire, smoke, and non-fire stimuli. Comprehensive field evaluations will validate the system's performance, demonstrating its potential to mitigate fire risks and support emergency response operations. This research contributes to the advancement of intelligent aerial surveillance technologies, highlighting the critical role of drones in modern fire detection and management strategies.