About this item
- Title
- Foul Detection for Table Tennis Serves Using Deep Learning
- Content partner
- Auckland University of Technology
- Collection
- Tuwhera
- Description
Detecting serve fouls in table tennis is critical for ensuring fair play. This paper explores the development of foul detection of table tennis serves by leveraging 3D ball trajectory analysis and deep learning techniques. Using a multi-camera setup and a custom dataset, we employed You Only Look Once (YOLO) models for ball detection and Transformers for critical trajectory point identification. We achieved 87.52% precision in detecting fast-moving balls and an F1 score of 0.93 in recognizing...
- Format
- Research paper
- Research format
- Journal article
- Date created
- 2024-12-25
- Creator
- Yang, Guang Liang / Nguyen, Minh / Yan, Wei Qi / Li, Xue Jun
- URL
- https://openrepository.aut.ac.nz/handle/10292/18483
- Related subjects
- 0906 Electrical and Electronic Engineering / 4009 Electronics, sensors and digital hardware
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
You can learn more about the rights status of this item at: https://creativecommons.org/licenses/by/4.0/
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Report this itemDigitalNZ brings together more than 30 million items from institutions so that they are easy to find and use. This information is the best information we could find on this item. This item was added on 09 January 2025, and updated 01 April 2025.
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