Date of Award
Santa Clara : Santa Clara University, 2021.
Computer Science and Engineering
Natural and human-caused disasters devastate and displace civilian populations. Over the past century, the rate at which these catastrophes occur has increased dramatically. Climate change and unsustainable human behaviors are large contributors to the occurrence of natural disasters, therefore it is likely this upward trend will continue. The region of the world where a disaster takes place often determines how severe the implications are for the affected civilians. The devastation that occurs from a disaster is much greater in low-resourced regions of the world.
Unmanned aerial vehicles (UAVs) are commonly used to assist first responders during disaster response. The existing UAV technologies focused on disaster recovery have proven to be quite effective, however, they are cost prohibitive, therefore limiting their use in developing regions of the world. Unlike the existing UAV-based disaster response technology, we propose a solution that enables indigenous first responders to reap the benefits of UAV-based disaster response technology.
Through extensive evaluation of cutting-edge single-board computers, UAV hardware, and computer vision models, we have created a UAV system that has comparable flight time and flight capabilities as the existing industry standard solutions. Additionally, our system capitalizes on the functional drawbacks of the current solutions. It is more modular, allowing for a single UAV to be used in a variety of disasters, and it boasts the capability of real-time computer vision. Most importantly, our system can be recreated for one-eighth the cost of a consumer alternative with similar functionality. As a result, first responders in low-resourced regions have access to affordable disaster response technology that can be used to save lives.
Azzarello, Connor; Gerbino, Chris; and Mehta, Ruchir, "Enhanced Sensing Methods for UAV-Based Disaster Recovery" (2021). Computer Science and Engineering Senior Theses. 194.