Chess Recognition Dataset (ChessReD)

<strong>Chess Recognition Dataset (ChessReD)</strong>The Chess Recognition Dataset (ChessReD) comprises a diverse collection of images of chess formations captured using smartphone cameras; a sensor choice made to ensure real-world applicability. <br>It was collected by capturing images of chessboards with various chess piece configurations. The chess opening theory was used to guarantee the variability of those configurations. In particular, the Encyclopaedia of Chess Openings (ECO) classifies opening sequences into five volumes with 100 subcategories each that are uniquely identified by an ECO code. 20 ECO codes were selected from each volume. Subsequently, each code of this set was randomly matched to an already played chess game that followed the particular opening sequence denoted by the ECO code; thus creating a set of 100 chess games. Finally, using the move-by-move information provided by Portable Game Notations (PGNs) that are used to record chess games, the selected games were played out on a physical chessboard with images being captured after each move.<br>Three distinct smartphone models were used to capture the images. Each model has different camera specifications, such as resolution and sensor type, that introduce further variability in the dataset. The images were also taken from diverse angles, ranging from top-view to oblique angles, and from different perspectives (e.g., white player perspective, side view, etc.). These conditions simulate real-world scenarios where chessboards can be captured from a bystander's arbitrary point of view. Additionally, the dataset includes images captured under different lighting conditions, with both natural and artificial light sources introducing these variations.<br>The dataset is accompanied by detailed annotations providing information about the chess pieces formation in the images. Therefore, the number of annotations for each image depends on the number of chess pieces depicted in it. There are 12 category ids in total (i.e., 6 piece types per colour) and the chessboard coordinates are in the form of algebraic notation strings (e.g., "a8"). These annotations were automatically extracted from Forsyth-Edwards Notations (FENs) that were available by the games' PGNs. Each FEN string describes the state of the chessboard after each move using algebraic notation for the piece types (e.g., "N" is knight) , capitalization for the piece colours (i.e., white pieces are denoted with uppercase letters, while black pieces with lowercase letters), and digits to denote the number of empty squares. Thus, by matching the captured images to the corresponding FENs, the state of the chessboard in each image was already known and annotations could be extracted. To further facilitate research in the chess recognition domain, bounding-box and chessboard corner annotations are also provided for a subset of 20 chess games. The corners are annotated based on their location on the chessboard (e.g., "bottom-left") with respect to the white player's view. This discrimination between these different types of corners provides information about the orientation of the chessboard that can be leveraged to determine the image's perspective and viewing angle.<br><strong>Dataset specifications</strong>The dataset consists of 100 chess games, each with an arbitrary number of moves and therefore images, amounting to a total of 10,800 images being collected. It was split into training, validation, and test sets following an 60/20/20 split, which led to a total of 6,479 training images, 2,192 validation images, and 2,129 test images. Since two consecutive images of a chess game differ only by one move, the split was performed on game-level to ensure that quite similar images would not end up in different sets. The split was also stratified over the three distinct smartphone cameras (Apple iPhone 12, Huawei P40 pro, Samsung Galaxy S8) that were used to capture the images. The three smartphone cameras introduced variations to the dataset based on the distinct characteristics of their sensors. For instance, while the image resolution for the Huawei phone was 3072x3072, the resolution for the remaining two models was 3024x3024.<br>While annotations about the position of the pieces in algebraic notation are available for every image in the dataset, bounding box and chessboard corner annotations are provided only for a subset of 20 randomly selected games from the train, validation, and test sets. For this subset a 70/15/15 split stratified over the smartphone cameras was followed, which led to a total of 14 training games (1,442 images), 3 validation games (330 images), and 3 test games (306 images) being annotated. This subset of ChessReD is denoted as ChessReD2K.<br>

<strong>国际象棋识别数据集（Chess Recognition Dataset, ChessReD）</strong>
本数据集收录了使用智能手机摄像头采集的多样化国际象棋棋局图像，选用手机传感器旨在确保数据集具备真实场景适用性。
数据集的采集过程为：针对不同棋子配置的国际象棋棋盘拍摄图像，并通过国际象棋开局理论确保棋局配置的多样性。具体而言，《国际象棋开局百科》（Encyclopaedia of Chess Openings, ECO）将开局序列划分为5卷，每卷包含100个子类别，每个类别由唯一的ECO代码标识。随后从每卷中选取20个ECO代码，将每个选中的代码随机匹配至一局已完成的、遵循该ECO代码对应开局序列的国际象棋对局，最终得到100组国际象棋对局。最后，借助用于记录国际象棋对局的可移植对局格式（Portable Game Notations, PGN）所提供的逐步对局信息，将选中的100局对局在实体棋盘上复现，并在每一步走棋后拍摄图像。
本次采集共使用三款不同型号的智能手机，每款手机的摄像头参数（如分辨率、传感器类型）各不相同，进一步提升了数据集的多样性。拍摄视角同样覆盖广泛范围，涵盖俯视至斜拍的多种角度，以及不同的观测视角（如白方视角、侧面视角等），以此模拟现实场景中旁观者任意角度拍摄棋盘的情况。此外，数据集还包含不同光照条件下拍摄的图像，涵盖自然光与人工光源两种场景，进一步丰富了数据分布。
本数据集附带详细标注信息，用于描述图像中的国际象棋棋子布局。因此，每张图像的标注数量取决于其中出现的棋子总数。标注共包含12个类别ID（即每方阵营各6种棋子类型），棋盘坐标采用代数记法字符串形式（例如"a8"）。上述标注信息可通过对局的PGN文件中提供的福赛斯-爱德华兹记法（Forsyth-Edwards Notation, FEN）自动提取。每条FEN字符串通过以下方式描述每步走棋后的棋盘状态：使用代数记法标识棋子类型（例如"N"代表骑士），通过大小写区分棋子阵营（白方棋子使用大写字母，黑方棋子使用小写字母），并用数字表示空白方格的数量。因此，通过将拍摄得到的图像与对应的FEN字符串进行匹配，即可获取每张图像对应的棋盘状态，并自动生成标注信息。为进一步推动国际象棋识别领域的研究，数据集还为其中20局对局的子集提供了边界框与棋盘角点标注。角点标注以白方视角为基准，按照其在棋盘上的位置进行标识（例如"左下角"）。通过区分不同位置的角点，可获取棋盘的朝向信息，进而推断图像的拍摄视角与角度。
<strong>数据集规格参数</strong>
本数据集共包含100局国际象棋对局，每局对局的走棋步数不限，因此对应的图像数量也各不相同，最终累计采集得到10800张图像。数据集按照60/20/20的比例划分为训练集、验证集与测试集，最终分别包含6479张训练图像、2192张验证图像与2129张测试图像。由于同一对局中连续两张图像仅相差一步走棋，相似度极高，因此本次划分以对局为单位进行，以避免相似图像被划分至不同数据集子集。同时，划分过程针对三款采集所用的智能手机（Apple iPhone 12、华为P40 Pro、三星Galaxy S8）进行了分层抽样，确保各子集内的设备分布均衡。三款手机的传感器特性各不相同，为数据集带来了额外的多样性。例如，华为P40 Pro的图像分辨率为3072×3072，其余两款手机的分辨率均为3024×3024。
尽管数据集中的每张图像均附带棋子位置的代数记法标注，但边界框与棋盘角点标注仅针对从训练、验证与测试集中随机选取的20局对局子集提供。该子集同样按照70/15/15的比例进行划分，并针对智能手机设备进行分层抽样，最终得到14局训练对局（含1442张图像）、3局验证对局（含330张图像）与3局测试对局（含306张图像）。该ChessReD子集被命名为ChessReD2K。

搜集汇总

背景与挑战

背景概述

Chess Recognition Dataset (ChessReD) 是一个包含10,800张国际象棋棋盘图像的数据集，由三种不同智能手机拍摄，涵盖多种角度和光照条件，适用于国际象棋识别研究。数据集提供了详细的棋子位置注释，并包含一个子集ChessReD2K，专门用于边界框和角落注释的研究。