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Abstract
Robots are a modern technology that is experiencing very rapid growth. Robots also have several types, one type of robot is Robot Vision. Robot Vision uses image processing and computer vision in carrying out its tasks, including recognizing objects and controlling robot movements. Taking the concept from the ABU (Asia Pacific Broadcasting Union) robot contest in 2024 where the robot is required to be able to pick up balls with red and blue colors which are then inserted into a place called SILO. The detection method used in this study uses the YOLO (You Only Look Once) method with the YOLOv5 version. The reason for choosing the YOLOv5 version is because of its ability to be more accurate than previous versions and lighter to run than newer versions. From the results of this study, it was found that the confidence value for each object was quite high, which was 0.90. In addition, the precision value against recall for all classes gets a value of 0.991 or 99.1%. For the ball and SILO classes, the precision to recall value is 0.993 or 99.3%. So it can be concluded that this method has good value in terms of detection and accuracy
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Copyright (c) 2024 Revanza Akmal Pradipta Akmal, Ryan Yudha Adhitya, Zindhu Maulana Ahmad Putra, Ii'Munadhif, Mohammad Abu Jami’in, Muhammad Khoirul Hasin
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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References
Y. Yang and T. Holvoet, “Generating safe autonomous Decision-Making in ROS,” arXiv (Cornell University), vol. 371, pp. 184–192, Sep. 2022, doi: 10.4204/eptcs.371.13.J. Breckling, Ed., The Analysis of Directional Time Series: Applications to Wind Speed and Direction, ser. Lecture Notes in Statistics. Berlin, Germany: Springer, 1989, vol. 61.
N. Aisyah et al., “Pengujian robot otomatis pendeteksi rintangan berbasis mikrokontroler,” Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi Dan Sistem Komputer/Jurnal Esensi Infokom, vol. 5, no. 2, pp. 1–6, Feb. 2022, doi: 10.55886/infokom.v5i2.264.
K. D. Setyanto, I. Fibriani, and S. Sumardi, “PENGENDALIAN MOBILE ROBOT VISION MENGGUNAKAN WEBCAM PADA OBJEK ARAH PANAH BERBASIS RASPBERRY PI,” May 02, 2016. https://jurnal.unej.ac.id/index.php/E-JAEI/article/view/2533
Asian Broadcasting Union, “Abu Robocon 2024,” Aburobocon2024. https://aburobocon2024.vtv.gov.vn/gamerules (accessed Apr. 17, 2024).
D. I. Mulyana and M. A. Rofik, “Implementasi deteksi real time klasifikasi jenis kendaraan di Indonesia menggunakan metode YOLOV5,” Jurnal Pendidikan Tambusai, vol. 6, no. 3, pp. 13971–13982, Jul. 2022, doi: 10.31004/jptam.v6i3.4825.
L. Suroiyah, Y. Rahmawati, and R. Dijaya, “FACEMASK DETECTION USING YOLO V5,” Jurnal Teknik Informatika, vol. 4, no. 6, pp. 1277–1286, Dec. 2023, doi: 10.52436/1.jutif.2023.4.6.1043.
H.-V. Nguyen, J.-H. Bae, Y. E. Lee, H. Lee, and K.-R. Kwon, “Comparison of Pre-Trained YOLO Models on Steel Surface Defects Detector Based on Transfer Learning with GPU-Based Embedded Devices,” Sensors, vol. 22, no. 24, p. 9926, Dec. 2022, doi: 10.3390/s22249926.
D.N. Nugroho and L. Anifah, “Perancangan Sistem Deteksi Objek Bola Dan Gawang Pada Robot Sepakbola Menggunakan Metode Darknet YOLO,” JIEET (Journal of Information Engineering and Educational Technology), vol. Volume 07 Nomor 01, 2023, Jun. 2023, doi: 10.26740/jieet.v7n1.p22-29.
S. Indolia, A. K. Goswami, S. P. Mishra, and P. Asopa, “Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach,” Procedia Comput. Sci., vol. 132, pp. 679–688, 2018, doi: 10.1016/j.procs.2018.05.069.
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W. Muldayani, “IMPLEMENTASI SISTEM OBJECT TRACKING UNTUK MENDETEKSI DUA OBJEK BERBASIS DEEP LEARNING,” Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer/Simetris, vol. 14, no. 1, pp. 1–14, Apr. 2023, doi: 10.24176/simet.v14i1.9236.
glenn-jocher, “Ultralytic-yolov5,” 2022. https://github.com/ultralytics/yolov5 (accessed April 20 , 2024).
H. Lin, J. D. Deng, D. Albers, and F. W. Siebert, “Helmet use detection of tracked motorcycles using CNN-Based Multi-Task learning,” IEEE Access, vol. 8, pp. 162073–162084, Jan. 2020, doi: 10.1109/access.2020.3021357.
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