Authors :
Rainier Ace H. Cabatuando; John Rick B. Maaño; John Randel C. Sorosoro; Tommy A. Ditucalan
Volume/Issue :
Volume 11 - 2026, Issue 4 - April
Google Scholar :
https://tinyurl.com/3bmw2r4h
Scribd :
https://tinyurl.com/y6ynhhba
DOI :
https://doi.org/10.38124/ijisrt/26apr1916
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
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.
Keywords :
AI-Powered Rescue Board; YOLO-Based Detection; Maritime Rescue; LoRa Communication; Emergency Response.
References :
- World Health Organization, "Global drowning report," WHO, Geneva, 2021.
- Asian Development Bank, "Philippines: Strengthening disaster resilience in coastal communities," ADB Publications, Manila, 2020.
- L. Quan, J. J. Bierens, R. Lis, A. Rowhani-Rahbar, and P. Morley, "Physiology and safety in water rescue operations," Resuscitation, vol. 146, pp. 112–120, 2020.
- V. H. Phung, T. T. Phan, and L. H. Nguyen, "Drowning and aquatic rescue challenges in Southeast Asia," J. Public Health and Safety, vol. 9, no. 3, pp. 211–219, 2018.
- Philippine Coast Guard, "Annual operational readiness report," PCG, 2022.
- UN Office for Coordination of Humanitarian Affairs, "Humanitarian needs overview: Philippines," UN OCHA, 2022.
- K. Nishiyama, Y. Sato, and M. Tanaka, "Smart camera systems for coastal emergency monitoring," Intl. J. Coastal Disaster Studies, vol. 12, no. 2, pp. 88–96, 2020.
- H. Luo, T. Wang, and L. Peng, "LED-based emergency illumination for maritime rescue," J. Marine Science and Technology, vol. 26, no. 4, pp. 556–565, 2021.
- B. Zhao et al., "Enhanced YOLO11 for drone-based maritime SAR object detection," PLOS ONE, vol. 20, no. 7, p. e0321920, 2025.
- R. S. Sinha, Y. Wei, and S. H. Hwang, "A survey on LoRa-based disaster response systems," IEEE Access, vol. 10, pp. 11412–11428, 2022.
- S. Tariq, T. Zia, and H. A. Khattak, "Smart flood rescue system using IoT and AI," J. Ambient Intelligence and Humanized Computing, vol. 12, no. 7, pp. 7143–7156, 2021.
- Y. Mao, J. Wang, and H. Li, "Design and application of USV-based SAR platform," Sensors, vol. 23, no. 4, p. 1948, 2023.
- Liquid Robotics, "Wave Glider marine autonomous system for ocean monitoring," 2019.
- R. Miranda, "UP-MSI deploys Wave Glider for Philippine marine surveillance," PCIEERD, 2019.
- L. M. Rañeses, K. A. Dela Cruz, and M. E. Santos, "Development of a remote-controlled aquatic drone for water monitoring in urban rivers," TUP-Manila, Undergrad. thesis, 2020.
- R. J. Gonzales and A. C. Pineda, "Design and implementation of a GSM-based coastal emergency assistance device," Batangas State University, Undergrad. thesis, 2021.
- W. Fang, L. Wang, and P. Ren, "Tinier-YOLO: Real-time object detection for constrained environments," IEEE Access, vol. 8, pp. 1935–1944, 2020.
- J. Li, J. Zhang, and X. Huang, "Development of Tinier-YOLO for real-time object detection on embedded systems," Sensors, vol. 19, no. 15, p. 3341, 2019.
- Semtech Corporation, "SX1276/77/78/79 LoRa transceivers datasheet," Semtech, 2020.
- X. Cao, Y. Li, and H. Zhang, "Improved YOLO11 for maritime object detection using SPD and CARAFE modules," Unpublished manuscript, 2025.
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.
Keywords :
AI-Powered Rescue Board; YOLO-Based Detection; Maritime Rescue; LoRa Communication; Emergency Response.