About This Project

This project aims to streamline disaster recovery by using drones with machine learning for thermal and audio object detection, enabling faster response. The drone’s thermal camera detects human heat signatures, even if they are buried, while machine learning enhances object and voice detection. Data is sent to a ground station for fast, accurate results, using GPS to pinpoint survivors. A GPS bracelet will help locate individuals more precisely.

Ask the Scientists

What is the context of this research?

Disasters, whether natural or man-made, can have devastating impacts on communities, resulting in loss of life, injuries, and widespread damage. In the aftermath of such events, rapid response and effective search and rescue operations are crucial to saving lives and providing necessary relief to affected individuals. Traditional methods of locating survivors often involve extensive ground searches, which can be time-consuming and inefficient, particularly in large or difficult-to-access areas.

What is the significance of this project?

In recent years, advancements in unmanned aerial vehicle (UAV) technology have opened up new possibilities for enhancing disaster response efforts. UAVs equipped with advanced cameras, sensors, ML-systems, real-time communication capabilities, and autonomous navigation systems can significantly improve the speed and accuracy of locating individuals in distress. By leveraging aerial views, these UAVs can cover vast areas quickly, identifying survivors, assessing damage, and guiding emergency response teams to critical locations.

However, the effective deployment of UAVs in disaster scenarios requires careful consideration of various factors, including environmental challenges, regulatory compliance, and integration with existing emergency response frameworks.

What are the goals of the project?

The funds will be used to build a complete and functional 5 inch freestyle drone. The expense of the drone parts is due to the quality of them, which needs to be very high, as the drone needs high speeds, allowing the drone to function better and meet our engineering goal and criteria.

Once completed, which we expect to take around a month, the project will be tested and documented. Testing will include going to Sunnyvale Baylands Park for at least 3 days and running 50 trials per day, documenting the weather, range of drone communications, accuracy of the machine learning models, average speed of the drone, and average accuracy of the drone waypoint system.

At maximum, the project will be completed by January 25th, 2024.