从图像中确定国际象棋游戏状态
Georg Wölflein1, Ognjen Arandjelović1
1School of Computer Science, University of St Andrews, North Haugh, St Andrews KY16 9SX, Scotland, UK.
Journal of imaging
|July 31, 2024
概括
这项研究引入了一种新的计算机视觉系统,用于从棋盘图像中准确识别象棋块. 它显著改进了现有方法,使国际象棋玩家能够进行自动游戏分析.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 从图像中准确识别棋子对自动化国际象棋分析和玩家改进至关重要.
- 现有的计算机视觉方法在准确性,大数据集和适应各种国际象棋集方面扎.
研究的目的:
- 开发一种使用计算机视觉和深度学习的新,准确和可适应的国际象棋识别系统.
- 创建一个大规模的合成数据集,用于训练强大的国际象棋识别模型.
主要方法:
- 一个基于RANSAC的算法用于棋盘本地化和转换为正规的网格.
- 两个卷积神经网络用于占用口罩预测和碎片分类.
- 一个短暂的转移学习方法,用于适应未见的棋盘.
主要成果:
- 该系统实现了0.23%的每平方误差率,超过了最先进的28倍.
- 数次射击转移学习方法在使用最小数据的新棋盘上达到99.83%的每平方准确率.
结论:
- 开发的系统在自动化国际象棋识别方面取得了重大进展.
- 该方法表现出高精度和适应性,为业余国际象棋运动员提供了实际应用.
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