Training dataset generation for bridge game registration
This work addresses the time-consuming manual data collection and labeling for card detection in bridge broadcasting, representing an incremental improvement in automating vision systems for specific game applications.
The paper tackles the problem of automating training dataset generation for playing card detection in bridge games, achieving 99.8% detection efficiency with a YOLOv4 network trained on the generated dataset.
This paper presents a method for automatic generation of a training dataset for a deep convolutional neural network used for playing card detection. The solution allows to skip the time-consuming processes of manual image collecting and labelling recognised objects. The YOLOv4 network trained on the generated dataset achieved an efficiency of 99.8% in the cards detection task. The proposed method is a part of a project that aims to automate the process of broadcasting duplicate bridge competitions using a vision system and neural networks.