Extraction of Stone Positions from a Sheet Image for Curling Match Database Construction

Curling is a sport in which two teams take turns delivering stones on ice and compete for total scores. It is a highly strategic sport, often referred to as “Chess on Ice”. In recent years, research on curling AI and statistical analysis aimed at tactical evaluation has been active. Decision-making in curling highly depends on the current stone position state, so obtaining stone positions is essential for tactical analysis. This study proposes an object detection model capable of acquiring stone coordinates with high accuracy and generality from stone position images of actual games. The proposed model was realized with a small amount of manually annotated data and pseudo-labeled images. Using the active testing method, the image-level accuracy of data—a strict criterion requiring perfect detection of all stones in a single image—for approximately 100,000 items was estimated to be 99.37%. Furthermore, we measured the positional error of the detected stones and found an average result of 0.472 px. We determined that this model had sufficient accuracy for practical use, so we decided to store the acquired coordinates in a database and use them as training data for the curling AI and statistical analysis.