Policy decision of curling refers to providing strategy suggestions for curling competition with the help of computers. Existing curling agents have achieved good results in the digital scenarios, but cannot make correct decisions when applied to actual competition and training scenes. In this paper, a strategies decision agent in the real scene has been proposed. The competition situation was acquired by a Situation-Aware Network and mapped by a Digital Extraction module. We designed Curling MCTS to explore the best strategy in continuous space. The effectiveness of our framework has been verified by experiments and evaluated by China’s wheelchair curling team at China Disabled Sports Management Center. With the help of our system, China’s wheelchair curling team trained effectively and won the championship in the XIII Paralympic Winter Games (2022, Beijing). In addition, a new curling target detection dataset was provided.

The movement data of curling targets is of great significance for the analysis and research of curling. However, in real-life curling competitions, the curling volume is limited and easy to be occluded, and the venue background illumination is complicated. To address these challenges, a curling target detection model, IFCD, based on Inverted Feature Extraction Network (IFNet) is proposed. IFNet allocates more resources to deal with high-resolution features without introducing additional computational burdens, thus avoiding feature loss caused by inappropriate downsampling. Moreover, a Dynamic Feature Fusion module is introduced in the Neck network to suppress background interference and reduce the feature confusion. In addition, the parameter-independent Four-Scale Decoupled Detection Head is introduced to reduce the conflict between classification and regression tasks and enhance the model’s multi-scale adaptability. IFCD achieves a 0.974 mAP@.5 (Mean Average Precision) on Curling, a regular curling dataset, and 0.723 mAP@.5 on Curling_hard, a complex curling dataset with numerous occlusions.