Análisis Comparativo de Tecnologías para Sistemas de Vigilancia Autónoma de Vehículos Acuáticos: Aplicabilidad en el Contexto Colombiano

Iván Camilo Leiton Murcia

Authors

Iván Camilo Leiton Murcia Centro de Desarrollo Tecnológico Naval (CEDNAV) https://orcid.org/0009-0007-9785-932X

Keywords:

Maritime surveillance, Vessel detection, Edge computing, Thermal imaging, Radar systems, Colombian waterways, Autonomous surveillance systems

Abstract

The monitoring and surveillance of aquatic vehicles has become increasingly important globally for ensuring national security, controlling maritime and fluvial traffic, preventing illicit activities, and protecting sensitive ecosystems. This study presents a comprehensive benchmarking analysis of key technologies for autonomous surveillance systems (ASS) specifically adapted to Colombian environmental conditions. Through systematic literature review and comparative analysis, we evaluate optical sensors (RGB and thermal cameras), radar systems, acoustic sensors, and processing architectures (edge vs. cloud computing) under the challenging operational scenarios typical of Colombia's diverse aquatic environments. The research methodology encompasses four critical technological domains: detection sensors, processing architectures, power systems, and communication technologies. Results indicate that edge computing architectures combined with hybrid sensor configurations (optical + thermal for general surveillance, radar + PTZ camera for high-security applications) provide optimal performance for Colombian conditions. The study concludes with specific recommendations for large-scale deployment considering the unique geographical, climatic, and infrastructure constraints of the Colombian territory.

Author Biography

Iván Camilo Leiton Murcia, Centro de Desarrollo Tecnológico Naval (CEDNAV)

Electronics engineer, graduated from the University of Ibagué, with experience in mechatronics, machine vision, artificial intelligence, and control engineering, areas in which he has developed highly demanding technical projects. He completed a Master’s degree in Renewable Energies at the Universidad Internacional de La Rioja (UNIR). His first professional experience was in a multinational company within the energy sector, while simultaneously participating in the design of printed circuit boards for research projects at the Universidad Cooperativa de Colombia, focused on Internet of Things (IoT) solutions for agriculture. During this period, he strengthened his programming skills and advanced artificial intelligence techniques. On July 8, 2024, he entered the Escuela Naval de Cadetes “Almirante Padilla” with the firm purpose of putting his knowledge at the service of the Colombian Navy. After being commissioned as an officer in December 2024, he was assigned to the Centro de Desarrollo Tecnológico Naval, where he actively participates in research, innovation, and technological development projects aimed at strengthening the institution’s strategic capabilities.

References

C. Gamage, R. Dinalankara, J. Samarabandu, et al., "A comprehensive survey on the applications of machine learning techniques on maritime surveillance to detect abnormal maritime vessel behaviors," WMU Journal of Maritime Affairs, vol. 22, no. 3, pp. 343-367, 2023.

A. M. Rekavandi, L. Xu, F. Boussaid, et al., "A guide to image-and video-based small object detection using deep learning: case study of maritime surveillance," IEEE Transactions on Intelligent Transportation Systems, vol. 26, no. 1, pp. 123-145, 2025.

H. Li, L. Deng, C. Yang, J. Liu, Z. Gu, "Enhanced YOLO v3 tiny network for real-time ship detection from visual image," IEEE Access, vol. 9, pp. 16890-16900, 2021.

R. W. Liu, Y. Guo, J. Nie, Q. Hu, Z. Xiong, et al., "Intelligent edge-enabled efficient multi-source data fusion for autonomous surface vehicles in maritime internet of things," IEEE Transactions on Green Communications and Networking, vol. 6, no. 2, pp. 1054-1064, 2022.

Y. Guo, Y. Lu, R. W. Liu, "Lightweight deep network-enabled real-time low-visibility enhancement for promoting vessel detection in maritime video surveillance," The Journal of Navigation, vol. 75, no. 2, pp. 230-250, 2022.

A. Sathiyamurthy, S. G. S. Naidu, "Designing edge computing solutions for real-time vessel tracking and collision avoidance," Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 16, no. 2, pp. 45-62, 2025.

V. G. Santos, D. S. Pereira, L. B. P. Nascimento, et al., "CNN-based boat detection for environmental protection area monitoring," in Congresso Brasileiro de Automática, 2022, pp. 1-8.

H. H. Helgesen, F. S. Leira, T. I. Fossen, T. A. Johansen, "Tracking of ocean surface objects from unmanned aerial vehicles with a pan/tilt unit using a thermal camera," Journal of Intelligent & Robotic Systems, vol. 91, no. 3-4, pp. 775-793, 2018.

A. A. S. AlMansoori, I. Swamidoss, S. I. Niwas, "Analysis of different tracking algorithms applied on thermal infrared imagery for maritime surveillance systems," in Artificial Intelligence and Machine Learning for Multi- Domain Operations Applications II, vol. 11543, 2020.

Ø. K. Helgesen, E. F. Brekke, A. Stahl, Ø. Engelhardtsen, "Low altitude georeferencing for imaging sensors in maritime tracking," IFAC-PapersOnLine, vol. 53, no. 2, pp. 14256-14261, 2020.

Z. Shao, H. Lyu, Y. Yin, T. Cheng, X. Gao, et al., "Multi-scale object detection model for autonomous ship navigation in maritime environment," Journal of Marine Science and Engineering, vol. 10, no. 11, p. 1783, 2022.

C. Zhang, F. Xiao, "Overview of data acquisition technology in underwater acoustic detection," Procedia Computer Science, vol. 183, pp. 509-516, 2021.

G. D. Hastie, G. M. Wu, S. Moss, P. Jepp, et al., "Automated detection and tracking of marine mammals: A novel sonar tool for monitoring effects of marine industry," Aquatic Conservation: Marine and Freshwater Ecosystems, vol. 29, no. 6, pp. 1019-1030, 2019.

L. P. Perera, P. Oliveira, C. G. Soares, "Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 3, pp. 1188-1200, 2012.

G. He, W. Wang, B. Shi, S. Liu, H. Xiang, et al., "An improved yolo v4 algorithm-based object detection method for maritime vessels," International Journal of Software Engineering and Applications, vol. 11, no. 4, pp. 23-35, 2022.

Z. Li, T. Liu, S. Li, L. You, S. Liang, et al., "An unmanned traffic command system for controlled waterway in inland river: an edge-centric IoT approach," Unmanned Systems, vol. 12, no. 3, pp. 445-462, 2024.

A. Copping, A. LiVecchi, H. Spence, et al., "Maritime renewable energy markets: power from the sea," Marine Technology Society Journal, vol. 52, no. 5, pp. 99-109, 2018.

J. C. Omondi, "Improving maritime surveillance in Kenya's remote coastal islands: application of renewable energy solutions," Master's thesis, World Maritime University, Malmö, Sweden, 2017.

F. Adamo, F. Attivissimo, C. G. C. Carducci, A. M. L. Lanzolla, "A smart sensor network for sea water quality monitoring," IEEE Sensors Journal, vol. 15, no. 5, pp. 2514-2522, 2014.

L. Delauney, C. Compere, M. Lehaitre, "Biofouling protection for marine environmental sensors," Ocean Science, vol. 6, no. 2, pp. 503-511, 2010.

M. A. Ullah, K. Mikhaylov, H. Alves, "Enabling mMTC in remote areas: LoRaWAN and LEO satellite integration for offshore wind farm monitoring," IEEE Transactions on Communications, vol. 69, no. 12, pp. 8744- 8758, 2021.

S. Pensieri, F. Viti, G. Moser, S. B. Serpico, A. Bordone, "Evaluating LoRaWAN connectivity in a marine scenario," Journal of Marine Science and Engineering, vol. 9, no. 11, p. 1218, 2021.