端到端国际象棋识别
End-to-End Chess Recognition
AI总结：
提出端到端深度学习方法直接从棋盘图像预测棋子配置，避免误差累积，并构建真实多角度数据集ChessReD，准确率比现有最优方法高约7倍。
摘要：
国际象棋识别是从棋盘图像中提取棋子配置的任务。当前的方法使用由独立模块组成的流水线，如棋盘检测、方格定位和棋子分类。相反，我们遵循深度学习理念，探索一种端到端方法直接从图像预测配置，从而避免顺序方法的误差累积并消除对中间标注的需求。此外，我们引入了一个新数据集Chess Recognition Dataset（ChessReD），包含10,800张真实照片及其对应标注。与现有合成渲染且角度有限的数据集不同，ChessReD使用智能手机摄像头从各种角度拍摄照片；这一传感器选择确保了现实世界的适用性。我们在引入的具有挑战性的基准数据集上的国际象棋识别方法优于相关方法，在ChessReD测试图像中成功识别了15.26%的棋子配置。这一准确率看似较低，但比当前最先进方法高约7倍，反映了问题的难度。代码和数据可通过以下链接获取：https://github.com/ThanosM97/end-to-end-chess-recognition。
英文摘要：
Chess recognition is the task of extracting the chess piece configuration from a chessboard image. Current approaches use a pipeline of separate, independent, modules such as chessboard detection, square localization, and piece classification. Instead, we follow the deep learning philosophy and explore an end-to-end approach to directly predict the configuration from the image, thus avoiding the error accumulation of the sequential approaches and eliminating the need for intermediate annotations. Furthermore, we introduce a new dataset, Chess Recognition Dataset (ChessReD), that consists of 10,800 real photographs and their corresponding annotations. In contrast to existing datasets that are synthetically rendered and have only limited angles, ChessReD has photographs captured from various angles using smartphone cameras; a sensor choice made to ensure real-world applicability. Our approach in chess recognition on the introduced challenging benchmark dataset outperforms related approaches, successfully recognizing the chess pieces' configuration in 15.26% of ChessReD's test images. This accuracy may seem low, but it is ~7x better than the current state-of-the-art and reflects the difficulty of the problem. The code and data are available through: https://github.com/ThanosM97/end-to-end-chess-recognition.
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。
