Revansyah Armadito, Soraya Norma Mustika, Arya Kusumawardana, Achmad Hamdan, Viola Malta Ramadhani, Safaa N Saud Al-Humairi
Illegal, Unreported and Unregulated (IUU) fishing is a multidimensional challenge that threatens the sustainability of global marine resources, particularly for Indonesia as the world's largest archipelagic state. One of the most destructive forms of IUU fishing is the use of bottom trawl nets, which can severely damage seabed ecosystems and endanger both fish populations and the livelihoods of small-scale fishers. Although regulations such as Ministerial Regulation No.2/2015 of the Ministry of Marine Affairs and Fisheries explicitly ban the use of trawls, violations remain prevalent, especially in remote coastal areas due to limited monitoring and surveillance technologies. This study proposes the development of an underwater monitoring system based on autonomous technology and artificial intelligence by integrating a Remotely Operated Vehicle (ROV) and an Autonomous Surface Vessel (ASV). The ROV is equipped with a Raspberry Pi controller and an underwater camera for capturing live video footage, which is transmitted to the ASV via User Datagram Protocol (UDP) communication over Cat5 LAN cable. The ASV, powered by Jetson Orin Nano, serves as the data processing and communication hub, enabling real-time object detection using the You Only Look Once version 8 (YOLOv8) algorithm - a deep learning method renowned for its speed and accuracy in object classification. The models were trained using Roboflow and showed excellent performance, with an accuracy rate of 95%. On testing, the system successfully detected 241 non-trawl objects and 115 trawl objects, which shows the effectiveness of the model in classifying fishing gear in real-time. © 2025 IEEE.
University of State Malang, Dept of Electrical Engineering, Blitar, Indonesia; University of State Malang, Dept of Electrical Engineering, Malang, Indonesia; Management and Science University, Dept of Research, Internationalisation, and Industry, Selangor, Malaysia
