Real-Time Accurate Determination of Table Tennis Ball and Evaluation of Player Stroke Effectiveness with Computer Vision-Based Deep Learning
Source abstract
Abstract
The adoption of artificial intelligence (AI) in sports training has the potential to revolutionize skill development, yet cost-effective solutions remain scarce, particularly in table tennis. To bridge this gap, we present an intelligent training system leveraging computer vision and machine learning for real-time performance analysis. The system integrates YOLOv5 for high-precision ball detection (98% accuracy) and MediaPipe for athlete posture evaluation. A dynamic time-wrapping algorithm further assesses stroke effectiveness, demonstrating statistically significant discrimination between beginner and intermediate players (p = 0.004 and Cohen’s d = 0.86) in a cohort of 50 participants. By automating feedback and reducing reliance on expert observation, this system offers a scalable tool for coaching, self-training, and sports analysis. Its modular design also allows adaptation to other racket sports, highlighting broader utility in athletic training and entertainment applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sports Performance and Training · Stroke Rehabilitation and Recovery
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Health and Physical Education, The Education University of Hong Kong, Hong Kong 999077, China
Center for Physical Education, Southern University of Science and Technology, Shenzhen 518055, China
Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Department of Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Department of Computer Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Funding
Teaching Reform Fund from the Southern University of Science and Technology · Cultivation of Guangdong College Students’ Scientific and Technological Innovation Fund
References (37)
Show source-provided references
1. Advances in the application of information technology to sport performance10.1080/026404102320675611 ↗
2. Wearable Sensors for Real-Time Kinematics Analysis in Sports: A Review10.1109/JSEN.2020.3019016 ↗
3. Virtual Reality Experience of Mega Sports Events: A Technology Acceptance Study10.3390/jtaer17020036 ↗
4. Predicting Sports Injuries with Wearable Technology and Data Analysis10.1007/s10796-020-10018-3 ↗
5. Giménez-Egido, J.M., Ortega, E., Verdu-Conesa, I., Cejudo, A., and Torres-Luque, G. (2020). Using Smart Sensors to Monitor Physical Activity and Technical–Tactical Actions in Junior Tennis Players. Int. J. Environ. Res. Public Health, 17.10.3390/ijerph17031068 ↗
6. Dokic, K., Mesic, T., and Martinovic, M. (2020). Table Tennis Forehand and Backhand Stroke Recognition Based on Neural Network. Proceedings of Advances in Computing and Data Sciences, Springer.10.1007/978-981-15-6634-9_3 ↗
7. Performance Improvement and Skill Transfer in Table Tennis Through Training in Virtual Reality10.1109/TVCG.2021.3086403 ↗
8. Lin, H.-I., Yu, Z., and Huang, Y.-C. (2020). Ball Tracking and Trajectory Prediction for Table-Tennis Robots. Sensors, 20.10.3390/s20020333 ↗
9. Mat Sanusi, K.A., Mitri, D.D., Limbu, B., and Klemke, R. (2021). Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks. Sensors, 21.10.3390/s21093121 ↗
10. Learning to Play Table Tennis From Scratch Using Muscular Robots10.1109/TRO.2022.3176207 ↗
11. Michalski, S.C., Szpak, A., Saredakis, D., Ross, T.J., Billinghurst, M., and Loetscher, T. (2019). Getting your game on: Using virtual reality to improve real table tennis skills. PLoS ONE, 14.10.31234/osf.io/aw5gk ↗
12. Real-time posture feedback for effective motor learning in table tennis in virtual reality10.1016/j.ijhcs.2021.102731 ↗
13. Abdo Abou, J. (2022). Table Tennis and Physics. Simulation Modeling—Recent Advances, New Perspectives, and Applications, IntechOpen. Chapter 11.
14. Low-cost system for real-time detection of the ball-table impact position on ping-pong table10.1016/j.apacoust.2022.108832 ↗
15. Rong, Z. (2024). Optimization of table tennis target detection algorithm guided by multi-scale feature fusion of deep learning. Sci. Rep., 14.10.1038/s41598-024-51865-3 ↗
16. Li, W., Liu, X., An, K., Qin, C., and Cheng, Y. (2023). Table Tennis Track Detection Based on Temporal Feature Multiplexing Network. Sensors, 23.10.3390/s23031726 ↗
17. Ning, T., Wang, C., Fu, M., and Duan, X. (2023). A study on table tennis landing point detection algorithm based on spatial domain information. Sci. Rep., 13.10.1038/s41598-023-42966-6 ↗
18. Goh, G.L., Goh, G.D., Pan, J.W., Teng, P.S., and Kong, P.W. (2023). Automated Service Height Fault Detection Using Computer Vision and Machine Learning for Badminton Matches. Sensors, 23.10.3390/s23249759 ↗
19. Messelodi, S., Modena, C.M., Ropele, V., Marcon, S., and Sgrò, M. (2019). A Low-Cost Computer Vision System for Real-Time Tennis Analysis. Proceedings of Image Analysis and Processing—ICIAP 2019, Springer International Publishing.10.1007/978-3-030-30642-7_10 ↗
20. Brocherie, F., and Dinu, D. (2022). Biomechanical estimation of tennis serve using inertial sensors: A case study. Front. Sports Act. Living, 4.10.3389/fspor.2022.962941 ↗
21. High-precision and rapid binocular camera calibration method using a single image per camera10.1364/OE.459379 ↗
22. Gomez-Gonzalez, S., Nemmour, Y., Schölkopf, B., and Peters, J. (2019). Reliable Real-Time Ball Tracking for Robot Table Tennis. Robotics, 8.10.3390/robotics8040090 ↗
23. Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Fang, J., and Michael, K. (2022). Ultralytics/yolov5: v6.1—TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference, Zenodo.
24. Jocher, G., Chaurasia, A., Stoken, A., Borovec, J., Kwon, Y., Michael, K., Fang, J. (2022). Ultralytics/Yolov5: v6.2—YOLOv5 Classification Models, Apple M1, Reproducibility, ClearML and Deci.ai Integrations, Zenodo.
25. 3D acquisition and stereo-camera calibration by active devices: A unique structured light encoding framework10.1016/j.optlaseng.2019.105989 ↗
26. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
27. Fang, J., Liu, Q., and Li, J. (2021, January 24–26). A Deployment Scheme of YOLOv5 with Inference Optimizations Based on the Triton Inference Server. Proceedings of the 2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), Chengdu, China.10.1109/ICCCBDA51879.2021.9442557 ↗
28. High Performance YOLOv5: Research on High Performance Target Detection Algorithm for Embedded Platform
29. Microsoft Kinect Sensor and Its Effect10.1109/MMUL.2012.24 ↗
30. Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M., and Lee, J. (2019, January 17). Mediapipe: A framework for perceiving and processing reality. Proceedings of the Third Workshop on Computer Vision for AR/VR at IEEE Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.
31. Efficient Detection Model of Steel Strip Surface Defects Based on YOLO-V710.1109/ACCESS.2022.3230894 ↗
32. Robust wavelet denoising10.1109/78.923297 ↗
33. Müller, M. (2007). Dynamic Time Warping. Information Retrieval for Music and Motion, Springer.10.1007/978-3-540-74048-3 ↗
34. Wong, D.W., Lee, W.C., and Lam, W.-K. (2020). Biomechanics of Table Tennis: A Systematic Scoping Review of Playing Levels and Maneuvers. Appl. Sci., 10.10.3390/app10155203 ↗
35. Mateus, N., Abade, E., Coutinho, D., Gómez, M.-Á., Peñas, C.L., and Sampaio, J. (2025). Empowering the Sports Scientist with Artificial Intelligence in Training, Performance, and Health Management. Sensors, 25.10.3390/s25010139 ↗
36. Exploring the Application of Artificial Intelligence in Sports Training: A Case Study Approach10.1155/2021/4658937 ↗
37. Naughton, M., Salmon, P.M., Compton, H.R., and McLean, S. (2024). Challenges and opportunities of artificial intelligence implementation within sports science and sports medicine teams. Front. Sports Act. Living, 6.10.3389/fspor.2024.1332427 ↗