2021/09/24 by Piotr Wzorek, Wzorek, Piotr, Tomasz Kryjak +1
Computer Science · Engineering · Medicine · #Artificial intelligence #Bridge (graph theory) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Geography #Human Motion and Animation #Human Pose and Action Recognition #Image and Video Processing (eess.IV) #Infrastructure Maintenance and Monitoring #Internal medicine #Machine Learning (cs.LG) #Medicine #Meteorology #Training (meteorology) #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.11861
Submitted to Zeszyty Studenckiego Towarzystwa Naukowego, ISSN 1732-0925
arxiv created 2021/09/24 · openalex publication_date 2021/09/24 · arxiv updated 2021/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.