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

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.