• Mochamad Althaf Pramasetya Perkasa Informatics, Engineering Faculty, Universitas Siliwangi, Indonesia
  • R. Reza El Akbar Informatics, Engineering Faculty, Universitas Siliwangi, Indonesia
  • Muhammad Al Husaini Informatics, Engineering Faculty, Universitas Siliwangi, Indonesia
  • Randi Rizal Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Malaysia
Keywords: Deep Learning, Parameter, Soccer, YOLO


In this study, a performance comparison between the YOLOv7, YOLOv8, and YOLOv9 models in identifying objects in soccer matches is conducted. Parameter adjustments based on GPU storage capacity were also evaluated. The results show that YOLOv8 performs better, with higher precision, recall, and F1-score values, especially in the "Ball" class, and an overall accuracy (mAP@0.5) of 87.4%. YOLOv9 also performs similarly to YOLOv8, but YOLOv8's higher mAP@0.5 value shows its superiority in detecting objects with varying degrees of confidence. Both models show significant improvement compared to YOLOv7 in overall object detection performance. Therefore, based on these results, YOLOv8 can be considered as the model that is close to the best performance in detecting objects in the dataset used.  This study not only provides insights into the performance and characteristics of the YOLOv7, YOLOv8, and YOLOv9 models in the context of object detection in soccer matches but also results in a dataset ready for additional analysis or for training deep learning models.


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How to Cite
M. Althaf Pramasetya Perkasa, R. R. El Akbar, M. Al Husaini, and R. Rizal, “VISUAL ENTITY OBJECT DETECTION SYSTEM IN SOCCER MATCHES BASED ON VARIOUS YOLO ARCHITECTURE”, J. Tek. Inform. (JUTIF), vol. 5, no. 3, pp. 811-820, Jun. 2024.