Detection of Human in Flame

Abstract

This report explores the development of a real-time human-in-flames detection system using a YOLOv8 deep learning model. A carefully curated dataset of video frames depicting human-in-flames scenarios with corresponding bounding boxes was utilized to train the model. We explored various hyperparameter configurations and training techniques to optimize its performance. The model's effectiveness was evaluated on a separate validation dataset not used for training. Metrics like accuracy, precision, recall, and F1-score were employed to analyze the model's ability to generalize to unseen scenarios and identify people engulfed in flames within video streams. The findings of this project demonstrate the potential of YOLOv8 for human-in-flames detection, potentially contributing to improved fire safety systems and quicker response times in emergency situations.