Vehicle Counting System on Ro-Ro Ferry Bengkalis Route Using YOLO26n and ByteTrack Algorithm

Muhamad Nasir; Tengku Musri; Eva Kurniawaty; Thomas Brian; Muhammad Rizki Kurniawan

doi:10.61628/jsce.v7i4.2848

Muhamad Nasir Politeknik Negeri Bengkalis
Tengku Musri Politeknik Negeri Bengkalis
Eva Kurniawaty Politeknik Negeri Bengkalis
Thomas Brian Politeknik Negeri Bengkalis
Muhammad Rizki Kurniawan Politeknik Negeri Bengkalis

DOI: https://doi.org/10.61628/jsce.v7i4.2848

Abstract

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.

References

Alamgir, R. M., Shuvro, A. A., Mushabbir, M. A., et al. (2022). Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh. arXiv preprint arXiv:2212.09144.

Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016). Simple Online and Realtime Tracking. Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 3464–3468.

Bochkovskiy, A., Wang, C. Y., and Liao, H. Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv preprint arXiv:2004.10934.

Cahyono, F. Y. A., Setiawan, R. A., and Razak, A. A. (2025). Real-Time Vehicle Detection and Counting Using YOLOv8 and ByteTrack Multi-Object Tracking on Surveillance Cameras. Journal Zetroem.

Diwan, T., Anirudh, G., and Tembhurne, J. V. (2023). Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 82, 9243–9275.

Fahrezi, M. M., and Laksana, E. A. (2024). Traffic flow and congestion detection with YOLOv8 and ByteTrack-based multi object tracking. Jurnal Teknik Informatika (JUTIF), 5(4), 253–261.

Gomaa, A., Minematsu, T., Abdelwahab, M. M., Abo-Zahhad, M., and Taniguchi, R. (2022). Faster CNN-based vehicle detection and counting strategy for fixed camera scenes. Multimedia Tools and Applications, 81, 25443–25471.

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.

He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.

Jocher, G., Chaurasia, A., and Qiu, J. (2023). Ultralytics YOLO by Ultralytics. GitHub Repository.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., and Berg, A. C. (2016). SSD: Single Shot MultiBox Detector. European Conference on Computer Vision (ECCV), pp. 21–37.

Ng, J. J., Michael Goh, K. O., and Tee, C. (2023). Traffic Impact Assessment System using Yolov5 and ByteTrack. Journal of Informatics and Web Engineering, 2(2), 168–188.

Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788.

Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv preprint arXiv:1804.02767.

Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems, 28.

Szeliski, R. (2022). Computer Vision: Algorithms and Applications. 2nd Edition. Springer.

Wang, C. Y., Bochkovskiy, A., and Liao, H. Y. M. (2023). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7464–7475.

Wojke, N., Bewley, A., and Paulus, D. (2017). Simple Online and Realtime Tracking with a Deep Association Metric. Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 3645–3649.

Wu, D., Liao, M. W., Zhang, W. T., et al. (2022). YOLOP: You Only Look Once for Panoptic Driving Perception. Machine Intelligence Research, 19, 550–562.

Zhang, Y. (2023). Real-time vehicle detection and tracking based on the combination of YOLOv7 and ByteTrack. Applied and Computational Engineering, 4(1), 267–271.

Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (2022). ByteTrack: Multi-Object Tracking by Associating Every Detection Box. European Conference on Computer Vision (ECCV), pp. 1–17.