BOUNDARY BALLS DETECTION SYSTEM FOR UNMANNED SURFACE VEHICLE USING YOLOV4-TINY - Hanggraeni, Diva Diansari; Candradewi, Ika; Auzan, Muhammad (IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 2026)
Unmanned Surface Vehicle (USV) technology requires a reference such as a camera to determine the direction of its motion. The classical digital image processing is still less accurate for detecting boundary objects due to changes in light intensity and the influence of the aquatic environment. This study used hyperparameter subdivision and learning rate analysis of YOLOv4-tiny to detect boundary balls and trajectory obstacles. The training model is used in video testing under various conditions to determine the system's robustness.The training was carried out with subdivision 8 and a learning rate of 0.00261, which obtained a mAP of 97.02%, 95.1% F1-score, and 87.73% avg IoU. This system is robust for various testing times in terms of the diversity of light intensity and object reflections as well as conditions when there are waves or not. The system can work optimally during the day with an average performance of 98.13% F1-score, 99.18% mAP, and 88.75% avg IoU. In the conditions where there are waves, the system shows that the performance is not much different when there are no waves, namely mAP 91.93%, F1-score 94.62%, and avg IoU 87.33%. The average detection processing speed on the Jetson Nano 4GB is 14.2 FPS.