<![CDATA[Vehicle transportation management on Ro-Ro ferries requires accurate information regarding the number and type of vehicles transported during the loading process. Conventional counting methods are still performed manually, which requires time and has the potential to produce human errors. This research proposes an automatic vehicle counting system on the Bengkalis Ro-Ro ferry route using YOLO26n object detection combined with ByteTrack multi-object tracking. The system processes video recordings from Ro-Ro ferry activities and detects three vehicle categories, namely cars, motorcycles, and trucks. The detection process uses YOLO26n with a confidence threshold of 0.25 and an image size of 640 pixels. ByteTrack is implemented to maintain vehicle identities and prevent duplicate counting when vehicles cross the counting line. The experimental results show that the proposed system successfully detected and counted 20 vehicles consisting of 5 cars, 10 motorcycles, and 5 trucks. The system achieved an average processing speed of 15 frames per second, indicating that the proposed approach can support real-time vehicle monitoring on Ro-Ro ferry transportation systems.]]>
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