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| Title | Ai-Enhanced Unmanned Aerial Vehicles For Search And Rescue Operations |
|---|---|
| ID_Doc | 7079 |
| Authors | Rashida Farsath K.; Jitha K.; Mohammed Marwan V.K.; Muhammed Ali Jouhar A.; Muhammed Farseen K.P.; Musrifa K.A. |
| Year | 2024 |
| Published | 2024 5th International Conference on Innovative Trends in Information Technology, ICITIIT 2024 |
| DOI | http://dx.doi.org/10.1109/ICITIIT61487.2024.10580372 |
| Abstract | This paper introduces a cutting-edge AI-empowered Unmanned Aerial Vehicle (UAV) system, enriched with state-of-the-art sensor technology, advanced image recognition algorithms, and autonomous navigation capabilities. The system represents a transformative approach to search and rescue operations, offering unparalleled precision and rapid response times. Our methodology encompasses multifaceted data collection techniques, including surveys, interviews, data mining, Internet of Things (IoT) sensors, and sophisticated video analytics. Machine learning and deep learning models are then applied to process and analyze this data, enabling real-time image recognition for precise target identification. The system's AI-driven autonomous navigation algorithms optimize mission planning, resulting in significantly reduced response times and heightened mission success rates. Extensive real-world tests and simulations validate the exceptional performance of the proposed AI-empowered UAV system. These tests underscore its capacity to expedite emergency response efforts in dynamic and challenging environments. In parallel, this paper addresses critical ethical considerations, em-emphasizing responsible data handling practices, and robust security measures to ensure the system's integrity in sensitive contexts. As exemplified through a compelling case study of successful rescue operations, this technology represents a groundbreaking advancement in the field. By bridging the gap between cutting- edge technology and life-saving applications, it holds the potential to redefine the landscape of search and rescue missions, ushering in an era of heightened efficiency, precision, and impact. © 2024 IEEE. |
| Author Keywords | Artificial Intelligence; Auto encoder; Autonomous Navigation; Data Privacy; Deep Learning; Deep Learning; Drones; Emergency Response; Ethics; Human-Machine Collaboration; Image Recognition Algorithms; Intrusion Detection System; IOT Sensors; Machine Learning; Random Forest; Restricted Boltzmann Network; Search and Rescue Operations; Sensor Technology; Smart City; Unmanned Aerial Vehicles; Video analytic IoT |
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