| Title |
Basketball board detection using YOLO algorithms / |
| Authors |
Širmenis, Juozas ; Lukoševičius, Mantas |
| Full Text |
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| Is Part of |
CEUR workshop proceedings: IVUS 2024: Information society and university studies 2024: proceedings of the 29th international conference on information society and university studies (IVUS 2023) Kaunas, Lithuania, May 17, 2024 / edited by: I. Veitaitė, A. Lopata, T. Krilavičius, M. Woźniak.. Aachen : CEUR-WS. 2024, vol. 3885, p. 239-248.. ISSN 1613-0073 |
| Keywords [eng] |
YOLO algorithm ; basketball board detection ; rim detection ; net detection ; shooting box detection ; machine learning ; computer vision |
| Abstract [eng] |
This research aims to detect basketball board, rim, net, and shooting box using different versions of the YOLO algorithm: YOLOv5, YOLOv7, and YOLOv9. The study found that the YOLOv9-C model provided the best performance with a precision of 0.996, recall of 1, mAP_0.5 of 0.995, and mAP_0.5-0.95 of 0.899. YOLOv7 demonstrated the fastest inference time for the detection task and the smallest model size. Meanwhile, YOLOv5 exhibited architectural flexibility and the potential to be the optimal model in terms of detection speed, model size, and precision. |
| Published |
Aachen : CEUR-WS |
| Type |
Conference paper |
| Language |
English |
| Publication date |
2024 |
| CC license |
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