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Automated Service Height Fault Detection Using Computer Vision and Machine Learning for Badminton Matches
Guo Liang Goh1, Guo Dong Goh1, Jing Wen Pan2,3
1School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel, Switzerland)
|December 23, 2023
Summary
This study introduces an AI-powered badminton service fault detection system. The computer vision system significantly improves accuracy in detecting service heights, enhancing fairness in the sport.
Area of Science:
- Sports Technology
- Computer Vision
- Machine Learning
Background:
- Accurate service height detection is crucial for maintaining fairness in badminton.
- Existing methods for service fault detection can be subjective and inconsistent.
Purpose of the Study:
- To develop and evaluate an automated system for detecting service faults in badminton using computer vision and machine learning.
- To enhance the accuracy and consistency of service height judgments in badminton matches.
Main Methods:
- Utilized a computer vision system with two cameras and a workstation, employing the YOLOv5 object detection model.
- Developed an algorithm to pinpoint shuttlecock impact events for precise height measurement.
- Benchmarked system accuracy against a high-sample-rate motion capture system and human judges.
Main Results:
- The automated system achieved a 58% accuracy rate in detecting service heights between 1.150 and 1.155 m.
- Outperformed human judges by 3.5 times, with human accuracy at 16% in comparative tests.
- Demonstrated substantial improvements in accuracy and consistency over human judgment.
Conclusions:
- The developed automated system offers a highly reliable solution for badminton service fault detection.
- Significantly enhances the precision and integrity of officiating in badminton.
- Represents a meaningful advancement in leveraging technology for sports officiation.
