A Model for Analyzing Shuttlecock Landing Position Using Real-Time Object Detection Techniques
Keywords:
Artificial intelligence, Object detection, Badminton, YOLOv8, Sports analysisAbstract
This research aims to develop an Artificial Intelligence (AI) system for detecting and analyzing badminton shuttlecock positions to enhance the efficiency of game planning and training (Scouting). The researchers developed an object detection model based on the YOLOv8 architecture and conducted comparative performance tests under various conditions. The results indicated that the model trained with a dataset of 9,800 images for 300 epochs yielded the highest performance, achieving a Precision of 95%, a Recall of 92%, and a minimum Validation Loss of 2.07. When integrated into a web application and tested by experts and sports science students, the system demonstrated the ability to accurately identify shuttlecock landing positions, classify court zones, and display statistical results. Overall user satisfaction was rated at a very good level, particularly regarding its utility for learning and the convenience of data utilization. However, limitations regarding processing speed and tracking stability in certain scenarios remain.
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