AI-Powered Remote-Controlled Rescue Board with Camera, Siren, and Lighting System for Maritime Emergency Response

This study presents the design, development, and evaluation of a remote-controlled rescue board integrated with an AI-powered camera, siren, and high-visibility lighting system for rapid maritime response in the Philippine context. The design addresses critical limitations of conventional coastal rescue tools, including delayed victim detection, limited rescuer safety, and poor operational visibility. A YOLO-based object detection model deployed on an NVIDIA Jetson Orin Nano platform enables real-time identification of individuals in distress or drowning victim. Dual brushless-motor propellers driven by electronic speed controllers provide stable navigation across varying water conditions, while LoRabased wireless communication ensures reliable remote control at distances up to 430 meters. Functional evaluations demonstrated an average AI detection accuracy of approximately 91%, consistent buoyancy and structural stability supporting loads up to 90 kg, battery endurance of 95–120 minutes, and a 20-second response time over 100-meter deployment distances. Comparative analysis showed marked improvements over traditional rescue equipment in response speed, rescuer safety, and victim identification accuracy. User acceptance testing yielded an overall mean score of 4.49, reflecting strong stakeholder confidence in the system's safety, reliability, and operational practicality. The findings confirm that AI-enhanced, remotely operated rescue platforms represent a technically feasible and practically advantageous alternative for maritime and flood emergency response.