AI-Powered Drones for Disaster Search and Rescue: A Review of Human Detection, Autonomous Navigation and Emergency Communication
Abstract
Earthquakes, floods, landslides and building collapses cause serious damage and loss of life, and locating trapped or stranded people quickly is one of the most critical tasks for rescue teams. Ground searches are slow and often dangerous in unstable or inaccessible areas. AI-powered drones offer a faster and safer alternative by combining aerial mobility with onboard cameras, thermal sensors, GPS and computer vision. This paper reviews the design of AI drones for disaster rescue. It examines aerial datasets and deep object detectors for recognizing people, vehicles and damaged structures from above, thermal imaging for finding victims at night or under light cover, coverage path planning for searching large areas systematically, simultaneous localization and mapping for operation where GPS is unreliable, and drone networks that relay communication when ground infrastructure fails. Practical constraints of battery life, payload, onboard computing and regulation are discussed. A five-layer architecture comprising sensing, onboard edge AI, navigation and control, communication and a ground control station is proposed, followed by a research gap analysis, objectives, comparison of detection models and a discussion of challenges. The review concludes that AI drones can cover large areas quickly, reach inaccessible locations and reduce risk to rescuers, making disaster response faster, safer and more effective.
