Advanced AI Framework for Fire and Smoke Detection Integrating YOLOv8 Object Detection and XGBoost Classification

Early fire detection is very crucial for preventing further fire accidents in industrial and residential areas. Traditional fire detection methods depend on sensors which create false alarms many times and limited detection accuracy. To address these challenges, This study presents an Advanced AI Framework which combines YOLOv8 for real time object detection with XGBoost for enhanced classification accuracy. Our research utilizes YOLOv8’s deep learning architecture to accurately detect fire and smoke in complex scenarios. The detected features are then processed predictions by differentiating fire and datasets covering various fire and smoke conditions. This study enhances AI-based fire detection by providing a high-precision, real-time solution suitable for intelligent monitoring systems. Future improvements focus on seamless real-time processing integration.