Fast and Efficient Algorithm for Tibetan Jiu Chess Gameplay Data Collecting Based on Improved YOLOv8n Model

Tibetan Jiu chess is a national intangible cultural heritage of People’s Republic of China. The digital game platform has collected some online game data, but due to life habits, most masters and enthusiasts prefer to play offline. To collect offline gameplay data efficiently and quickly for Jiu chess, this paper proposes the intelligent algorithm based on improved You Only Look Once (YOLO) version 8 nano (YOLOv8n) model. Firstly, in order to improve the multi-scale feature fusion ability, the backbone and neck are replaced with co-designing and scaling ConvNets with masked autoencoders (namely ConvNeXt V2) and Gather-and-Distribute (GD) mechanism, respectively. Secondly, to reduce the parameter size of the optimized model, the Layer-Adaptive Magnitude-based Pruning (LAMP) score channel pruning algorithm is proposed. Finally, in order to convert the output of chess piece detection into standard chess manual data, the nearest Euclidean distance information matching algorithm and the game conversion algorithms are also proposed, separately. The experimental data source comes from the video recorded in 2023 National Tibetan Jiu Chess Competition in Qinghai Province. The experimental results show that the accuracy of our proposed model reaches 99.660% compared with other models, and the detection time after pruning is reduced by 5.5 ms. The results confirm that our algorithm can effectively improve and balance the speed and accuracy of chess piece detection.