AeroFlameGuard is an autonomous drone fire detection algorithm that uses computer vision-based fire detection systems utilizing machine learning algorithms to detect fire in images and videos in real-time. The primary goal of this project is to provide a reliable and fast fire detection solution that can be easily integrated into various applications and platforms.

The Fire Detection algorithm takes video feed from a drone that has a Raspberry Pi attached to it and runs the algorithm on a local machine. This is a small implementation of what could potentially be used in the fire prevention field in the future.

In recent years, California has faced an escalating wildfire crisis. To address this challenge, our Software Development Group, The Tie Fighters, consisting of Nathan Maldonado, Koa Afusia, and myself, Tanner Ensign, have developed an autonomous flying drone capable of navigating to specific GPS coordinates, hovering over a fire, and utilizing the cutting-edge YOLOv5 algorithm to detect the presence of flames. By leveraging this advanced technology, we aim to significantly improve wildfire response times while minimizing the risk to human lives involved in the detection and assessment process.