Main Article Content
Abstract
This research enhances the performance of a wheeled soccer robot in the RoboCup Middle Size League (KRSBI-B) by integrating Stereo Vision cameras, YOLOv5, and Harris Corner Detection for precise distance estimation and object detection. The objective is to improve the robot's ability to accurately recognize and measure the distance of objects, particularly the ball and opposing robots. Using image processing, the system significantly enhances real-time object detection, improving decision-making during the match. The YOLOv5 algorithm, trained with 4,000 labeled images, achieved impressive accuracy with confidence levels up to 0.99 for ball detection at 250 cm. Results show strong correlations between high-confidence detections and accurate distance estimations, enabling effective responses to dynamic match situations. This system provides a competitive edge, improving responsiveness, adaptability, and gameplay strategies, supporting its application in real-world robotic competitions.
Article Details
Copyright (c) 2025 Adi Rahmad Ramadhan, Agus Khumaidi, Mustika Kurnia Mayangsari, Mat Syai’in, Imam Sutrisno, Aulia Rahma Annisa
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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References
Purnomo. M. H. Mozef. E. Rochardjo. H. S. B. Prabowo. G. Purwanto. D. Pitowarno. E. ndra, I. Mutijarsa. K. dan Muis. Abdul. Kusumoputro. Benyamin, Buku Pedoman Kontes Robot Indonesia (KRI) Tahun 2023. Balai Pengembangan Talenta Indonesia Pusat Prestasi Nasional Kementerian Pendidikan, Kebudayaan, Riset Dan Teknologi. Jakarta, 2023.
F. Li and P. Li, “Computer-aided teaching software of three-dimensional model of sports movement based on kinect depth data,” Comput Aided Des Appl, vol. 18, no. S2, pp. 123–134, 2020, doi: 10.14733/cadaps.2021.S2.123-134.
A. Biswas, B. Dey, B. Poudyel, N. Sarkar, and T. Olariu, “Automatic fall detection using Orbbec Astra 3D pro depth images,” Journal of Intelligent and Fuzzy Systems, vol. 43, no. 2, pp. 1707–1715, 2022, doi: 10.3233/JIFS-219272.
L. Mejias, I. Fernando Mondragón, P. Doherty, P. Campoy, and I. Mondragón, “Stereo Vision-Based Navigation for an Autonomous Helicopter,” 2019. [Online]. Available: http://eprints.qut.edu.au/
M. Mansour, P. Davidson, O. Stepanov, and R. Piché, “Relative importance of binocular disparity and motion parallax for depth estimation: A computer vision approach,” Remote Sens (Basel), vol. 11, no. 17, 2019, doi: 10.3390/rs11171990.
F. Romadloni, J. Endrasmono, Z. M. A. Putra, A. Khumaidi, I. Rachman, and R. Y. Adhitya, “Identifikasi Warna Buoy Menggunakan Metode You Only Look Once Pada Unmanned Surface Vehicle,” Jurnal Teknik Elektro dan Komputer TRIAC, vol. 10, no. 1, 2023, doi: 10.21107/triac.v10i1.19650.
H. A. Saputri, M. Avrillio, L. Christofer, V. Simanjaya, and I. N. Alam, “Implementation of YOLO v7 algorithm in estimating traffic flow in Malang,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 117–126. doi: 10.1016/j.procs.2024.10.235.
C.-L. Ji, T. Yu, P. Gao, F. Wang, and R.-Y. Yuan, “YOLO-TLA: An Efficient and Lightweight Small Object Detection Model based on YOLOv5,” Feb. 2024, doi: 10.1007/s11554-024-01519-4.
M. A. Adi, Agus Khumaidi, Mohammad Basuki Rahmat, Dimas Pristovani Riananda, Muhammad Khoirul Hasin, and Didik Sukoco, “Implementasi Sistem Deteksi Titik Api Pada area graving dock Menggunakan YOLOv5,” Jurnal Elektronika dan Otomasi Industri, vol. 11, no. 2, pp. 473–482, Jul. 2024, doi: 10.33795/elkolind.v11i2.5233.
A. A. Wahyudi, Agus Khumaidi, Mohammad Basuki Rahmat, Dimas Pristovani Riananda, Mat Syai’in, and Joko Endrasmono, “Implementasi Robot Operating System (ROS) Untuk Meningkatkan Akurasi Deteksi Bola Menggunakan YOLO V5 Pada KRSBI-Beroda,” Jurnal Elektronika dan Otomasi Industri, vol. 11, no. 2, pp. 648–661, Jul. 2024, doi: 10.33795/elkolind.v11i2.5234.
Z. Tang, P. Wang, Y. Wang, C. Wang, and Y. Han, “Distributed Small-Step Path Planning and Detection Method for Post-earthquake Robot to Inspect and Evaluate Building Damage,” Front Neurorobot, vol. 16, Aug. 2022, doi: 10.3389/fnbot.2022.915150.
R. Ginting, R. Patmasari, and S. Aulia, “Sistem Orientasi Objek Dengan Metode Stereo Vision Berbasis Raspberry Pi,” Journal Research and Development (ITJRD), vol. 3, no. 1, 2018, doi: 10.25299/itjrd.2018.vol3(1).xxxx.
J. Maltar, “Robust visual place recognition using deep representations and sequence-based image matching,” 2023. [Online]. Available: https://urn.nsk.hr/urn:nbn:hr:168:950209
I. Marzuqi, G. Prilian Arinata, Z. Putra, and dan Agus Khumaidi, Segmentasi dan Estimasi Jarak Bola dengan Robot Menggunakan Stereo Vision. 2017.
X. Sun et al., “Distance Measurement System Based on Binocular Stereo Vision,” in IOP Conference Series: Earth and Environmental Science, Institute of Physics Publishing, Jul. 2019. doi: 10.1088/1755-1315/252/5/052051.