Ball Detection in Wheeled Soccer Robot Using the YOLOv8 Model

Aqza Tri Ananda HAT, Shoffin Nahwa Utama, M. Imamudin, Yunifa Miftachul Arif, Ajib Hanani

Abstract


This research designs and builds a wheeled soccer robot using YOLOv8 for real-time ball detection and distance estimation, aiming to improve efficiency in technology competitions. The system includes Arduino Uno R3, Raspberry Pi 3 model b, detection system, and navigation design. 691 ball image use as dataset that consist of 552 image as training dataset and 249 image as valid dataset. YOLOv8 demonstrated exceptional reliability in ball detection during testing, achieving an average accuracy of 100%, 100% precision, and 94% recall. Navigation testing toward the ball had an acceptable average error of 8.0466%. The results confirm that YOLOv8 is excellent for simplifying high-accuracy ball detection and distance estimation in wheeled soccer robots. Future work should consider a higher-spec Raspberry Pi, a high-resolution camera, additional sensors, and advanced systems to improve detection and obstacle avoidance (opponent robots, goal).

Keywords


Object Detection;Robot Navigation;Wheeled Soccer Robot;YOLOv8

Full Text:

PDF

References


[1] M. Kulshreshtha, S. S. Chandra, P. Randhawa, G. Tsaramirsis, A. Khadidos, and A. O. Khadidos, “Oatcr: Outdoor autonomous trash-collecting robot design using yolov4-tiny,” Electron., vol. 10, no. 18, 2021, doi: 10.3390/electronics10182292.
[2] D. Diono, M. J. W. Wicaksono, A. Jefiza, and D. R. Prayudha, “Pendeteksian Objek Hasil Pengepresan Kaleng dan Botol dengan Metode You Only Look Once (YOLO) yang Diaplikasikan pada Mesin Sortir Pembelajaran PBL,” J. Integr., vol. 16, no. 1, pp. 1–10, 2024, doi: 10.30871/ji.v16i1.4598.
[3] H. Soebhakti, S. Prayoga, R. A. Fatekha, and M. B. Fashla, “The Real-Time Object Detection System on Mobile Soccer Robot using YOLO v3,” Proc. 2019 2nd Int. Conf. Appl. Eng. ICAE 2019, 2019, doi: 10.1109/ICAE47758.2019.9221734.
[4] U. Aulia, I. Hasanuddin, M. Dirhamsyah, and N. Nasaruddin, “Heliyon A new CNN-BASED object detection system for autonomous mobile robots based on real-world vehicle datasets,” Heliyon, vol. 10, no. 15, p. e35247, 2024, doi:10.1016/j.heliyon. 2024.e35247.
[5] S. Susanto, F. A. Putra, and R. Analia, “XNOR-YOLO: The high precision of the ball and goal detecting on the barelang-FC robot soccer,” Proc. ICAE 2020 - 3rd Int. Conf. Appl. Eng., 2020, doi: 10.1109/ ICAE50557.2020.9350386.
[6] W. Lee, K. Kim, W. Ahn, J. Kim, and D. Jeon, “A Real-Time Object Detection Processor With XNOR -Based Variable-Precision Computing Unit,” IEEE Trans. Very Large Scale Integr. Syst., vol. 31, no. 6, pp. 749–761, 2023, doi:10.1109/TVLSI.2023.3257198.
[7] T. Bräunl, Robots and Controllers. 2022. doi: 10.1007/978-981-16-0804-9_1.
[8] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection”.
[9] A. C. Nugraha, M. L. Hakim, S. Yatmono, and M. Khairudin, “Development of Ball Detection System with YOLOv3 in a Humanoid Soccer Robot,” J. Phys. Conf. Ser., vol. 2111, no. 1, pp. 1–17, 2021, doi: 10.1088/1742-6596/2111/1/012055.
[10] F. F. Sanubari and R. D. Puriyanto, “Deteksi Bola dan Gawang dengan Metode YOLO Menggunakan Kamera Omnidirectional pada Robot KRSBI-B,” Bul. Ilm. Sarj. Tek. Elektro, vol. 4, no. 2, pp. 76–85, 2022, doi: 10.12928/ biste.v4i2.6712.
[11] H. Jati, N. A. Ilyasa, and D. D. Dominic, “Enhancing Humanoid Robot Soccer Ball Tracking, Goal Alignment, and Robot Avoidance Using YOLO-NAS,” J. Robot. Control, vol. 5, no. 3, pp. 829–838, 2024, doi: 10.18196/jrc.v5i3.21839.
[12] Д. Л. Я. Мобильного, Р. С. Использованием, and Y. И. Strong, “Real-Time Object Detection And Tracking For Mobile Robot Using YOLOv8 and Strong Sort,” vol. 11, no. 116, 2023.
[13] E. Upton and G. Halfacree, Raspberry Pi® User Guide. 2016. doi: 10.1002/ 9781119415572.
[14] K. W. Humaidillah, “Modul Belajar Arduino Uno,” p. 52, 2019.
[15] C. D. Manning, “Introduction to Information Retrieval,” no. c, 2009, [Online]. Available: https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf
[16] A. Kurniawan and A. Harumwidiah, “An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya,” vol. 22, no. 3, pp. 1245–1250, 2021, doi: 10.11591/ijeecs.v22.i3.pp1245-1250.





DOI: https://doi.org/10.18860/ijeie.v1i2.39317

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

IJEIE : International Journal of Electrical and Intelligent Engineering
Mailing Address
Department of Electrical Engineering
Faculty of Science and Technology
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Gajayana Street 50 Malang 65144, Jawa Timur, Indonesia
Email: ijeie@uin-malang.ac.id  

This work is licensed under Creative Commons Attribution-ShareAlike 4.0 International