计算机工程2025，Vol.51Issue(2)：102-110,9.DOI:10.19678/j.issn.1000-3428.0069106
AI-Curling:一种冰壶现场分析与决策方法
AI-Curling:An On-Site Curling Analysis and Decision-Making Method
摘要
Abstract
In response to the need for intelligent curling training,a new on-site curling decision-making method that combines computer vision and deep Reinforcement Learning(RL)technologies,Artificial Intelligence(AI)-Curling,is proposed.AI-Curling comprises two components:SR-Yolo for curling detection and Global Strategy Perception-Monte Carlo Tree Search(GSP-MCTS)for strategy generation.The former is responsible for sensing the state of the curling stones at critical moments and extracting information on the location and type of stones in real scenes.To improve the detection accuracy of small targets in large scenes and prevent feature loss due to inappropriate downsampling,a Shallow Refinement Backbone Network(SRNet)is introduced to capture richer feature information by adding layers during the initial stages of the network.An Adaptive Feature Optimization Fusion(AFOF)module is further introduced into the multiscale fusion network to increase the number of effective samples in each layer,thereby preventing small-scale targets from being submerged in complex backgrounds and noise.In the strategy generation module,curling match decision analysis is implemented using a combination of the MCTS algorithm and policy value network.A GSP module is embedded into the policy value network to enhance network spatial perception by introducing a kernel function to deal with action space continuity and execution uncertainty.In the experiments,SR-Yolo achieved 0.974 mAP@0.5 on the standard Curling dataset and 0.723 mAP@0.5 on the more complex obstructed Curling_hard dataset.In addition,GSP-MCTS achieved a 62％winning percentage compared with the latest real-scene curling model Curling MCTS,indicating that GSP-MCTS has superior performance.关键词
强化学习/深度学习/冰壶检测/小目标检测/蒙特卡洛树搜索Key words
Reinforcement Learning(RL)/deep learning/curling detection/small object detection/Monte Carlo Tree Search(MCTS)分类
信息技术与安全科学引用本文复制引用
孙浩淼,李宗民,肖倩,孙文洁,张雯欣..AI-Curling:一种冰壶现场分析与决策方法[J].计算机工程,2025,51(2):102-110,9.基金项目
国家重点研发计划(2019YFF0301800) （2019YFF0301800）
国家自然科学基金(61379106) （61379106）
山东省自然科学基金(ZR2013FM036,ZR2015FM011). （ZR2013FM036,ZR2015FM011）
