Automated Service Height Fault Detection Using Computer Vision and Machine Learning for Badminton Matches.

ABSTRACT: In badminton, accurate service height detection is critical for ensuring fairness. We developed an automated service fault detection system that employed computer vision and machine learning, specifically utilizing the YOLOv5 object detection model. Comprising two cameras and a workstation, our system identifies elements, such as shuttlecocks, rackets, players, and players' shoes. We developed an algorithm that can pinpoint the shuttlecock hitting event to capture its height information. To assess the accuracy of the new system, we benchmarked the results against a high sample-rate motion capture system and conducted a comparative analysis with eight human judges that used a fixed height service tool in a backhand low service situation. Our findings revealed a substantial enhancement in ac

SUBMITTER: Goh GL 

PROVIDER: S-EPMC10747833 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature