Life saver Drone: AI-Based Search and Rescue with Real-Time Alerts

Abstract

Through our research, we found that many existing drone systems do not clearly solve the problems related to thermal detection in disaster areas, and they often fail in providing reliable real-time human detection. These issues are important to address because search and rescue operations need to be more efficient, time-saving, and economic. Since UAVs are extremely useful in locations where humans cannot reach easily, improving their performance in such missions becomes essential for future emergency response systems. In this work, we propose a drone system designed to detect humans more effectively by using audio, video, and thermal inputs. Our approach focuses on integrating GPS, computer vision, and artificial intelligence to automate the drone’s operation and support real-time data processing. The drone captures photos, videos, audio, or thermal images, and the AI model confirms whether a human is present and potentially in need. Once detection is verified, the system immediately processes and sends the information to rescue teams in real time. It can also hold its position or circle around the target until responders arrive. The implications of this system show that faster alerts, improved detection accuracy, and economic design can significantly support rescue teams and help save more lives during emergencies.