The use of submarine cables as underwater transmission channels for distributing electrical energy in Indonesian waters is crucial. However, the detection and maintenance of submarine cables still heavily rely on human observation, leading to limitations in time and subjective interpretations. This research aims to design and implement an underwater object detection system based on YOLOv4 integrated with a Graphical User Interface (GUI) on a Remotely Operated Vehicle (ROV) for submarine cable detection. The YOLOv4 model was trained using a balanced dataset, achieving performance with precision of 0.89, recall of 0.85, and f1-score of 0.87. Detection of Good Condition (SC-Good-Condition) achieved an Average Precision (AP) of 97.62%, while Bad Condition detection (SC-Bad-Condition) had an AP of 87.54%, resulting in an overall mAP of 92.58%. The implemented GUI successfully detected submarine cables in two test videos with FPS rates of 0.178 and 0.083. The designed underwater object detection system using YOLOv4 and GUI on ROV demonstrated satisfactory performance in detecting submarine cables. However, further efforts are needed to improve the GUI's FPS to make it more responsive and efficient. This research contributes to the development of underwater detection technology that supports environmental observation and electrical energy distribution in Indonesian waters.
