Abstract: A real-time detection algorithm Light-YOLOv8 based on a combination of three-dimensional attention mechanism and lightweight convolution is proposed to achieve high-precision real-time chessboard recording during Go games.On the basis of YOLOv8,PWConv+PConv is used to replace the 3*3 convolution of the cross stage local network in the backbone network,which greatly reduce the computational complexity of the model.Adding CARAFE upsampling operaor and SimAM three-dimensional attention mechanism to improve the detection ability of small Go targets.The use of the Wise-IOU loss function improves the model's localization ability and convergence speed,and improves its detection ability in cases of chess piece adhesion,chess piece overlap,and uneven lighting.Optimize and compress the model for mobile deployment and deploy it on different Android devices,with an image resolution of 640*480.The average single detection time combined with image preprocessing and post-processing operations is 89ms,and the average detection frame rate is 37.6 fps.Conduct 50 rounds of score recording experiments,with an average score recording accuracy of over 97% and an average winner/loser discrimination accuracy of 100%,which can achieve stable go chess score recording function.

Key words: Object detection, Chess game recognition, Real time notation, YOLOv8, Lightweight network, Mobile devices