Enhancing Disaster Response Efforts with YOLOv8-based Human Detection in Mobile Robotics

Alnabulseih, khaled; Abd-EL-Rehim, Abd-EL-Rahman; Badawi, Mohamed Badawi; Elgohary, Rania

doi:10.21608/iiis.2025.292458.1036

	Enhancing Disaster Response Efforts with YOLOv8-based Human Detection in Mobile Robotics
International Integrated Intelligent Systems
Article 5, Volume 2, Issue 1, January 2025 PDF (370.15 K)
Document Type: Original Article
DOI: 10.21608/iiis.2025.292458.1036
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Authors
khaled Alnabulseih ¹; Abd-EL-Rahman Abd-EL-Rehim¹; Mohamed Badawi Badawi ²; Rania Elgohary³
¹Department of Artificial intelligence, Misr university for science and technology
²Department of Artificial Intelligence, College of Information Technology, Misr University for Science and Technology (MUST), 6th of October City 12566, Egypt
³Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt
Abstract
In the aftermath of natural disasters, the swift detection of individuals trapped beneath debris is crucial for successful rescue operations. This paper presents a Mobile Controlled Robot with advanced human detection capabilities designed to expedite search and rescue missions, emphasizing the importance of rapid response to save lives. Utilizing a YOLOv8 model with 90% accuracy, the robot analyzes real-time images captured by a webcam to detect human forms and movements, triggering a buzzer alert to notify rescue teams upon identifying potential victims. The robot’s remote operation via a mobile interface enhances flexibility and adaptability in complex terrains, allowing rescue personnel to control it from a safe distance. Equipped with all-terrain wheels, obstacle-avoidance sensors, and a thermal imaging camera, the robot can navigate through rubble and confined spaces, even in low visibility conditions. The mobile interface provides real-time video feed and sensor data to the rescue team, enabling quick, informed decision-making. The robot’s modular design allows for easy upgrades and maintenance, making it a cost-effective long-term solution. Rigorous testing has demonstrated the system’s efficacy and reliability in accurately locating trapped individuals, offering a promising improvement in the efficiency and effectiveness of disaster response operations.
Keywords
Robotics; YOLO; Deep learning; Fire rescue


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