LiveChess2FEN：一个基于CNN的棋子分类框架
LiveChess2FEN: a Framework for Classifying Chess Pieces based on CNNs
摘要：
利用计算机视觉实现国际象棋比赛的自动数字化是一项重大的技术挑战。这个问题对于比赛组织者和业余或专业玩家来说非常感兴趣，可以在线广播他们的OTB游戏或使用国际象棋引擎分析它们。以前的工作已经显示出有希望的结果，但识别准确性和最先进技术的延迟仍然需要进一步增强，以允许其实用和负担得起的部署。我们已经研究了如何在Nvidia Jetson Nano单板计算机上有效地实现它们。我们的第一个贡献是加速棋盘的检测算法。随后，我们分析了用于棋子分类的不同卷积神经网络以及如何在我们的嵌入式平台上有效地映射它们。值得注意的是，我们已经实现了一个功能框架，可以在不到1秒的时间内自动数字化图像中的国际象棋位置，在分类棋子时的准确率为92%，在检测棋盘时的准确率为95%。
英文摘要：
Automatic digitization of chess games using computer vision is a significant technological challenge. This problem is of much interest for tournament organizers and amateur or professional players to broadcast their over-the-board (OTB) games online or analyze them using chess engines. Previous work has shown promising results, but the recognition accuracy and the latency of state-of-the-art techniques still need further enhancements to allow their practical and affordable deployment. We have investigated how to implement them on an Nvidia Jetson Nano single-board computer effectively. Our first contribution has been accelerating the chessboard's detection algorithm. Subsequently, we have analyzed different Convolutional Neural Networks for chess piece classification and how to map them efficiently on our embedded platform. Notably, we have implemented a functional framework that automatically digitizes a chess position from an image in less than 1 second, with 92% accuracy when classifying the pieces and 95% when detecting the board.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
