A Human-Robot Interactive Mahjong Playing System Based on Visual Recognition Using a Convolutional Neural Network

Wu, Zhiyong; Han, Jiuqiang; Liu, Erhu; Lyu, Hongqiang

doi:10.1145/3487075.3487188

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…Constructing a high quality mahjong dataset is essential for training and evaluating mahjong recognition models. The CNN-based method usually require amount of data [4]. Here are the key steps in building the mahjong card dataset:…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

“…YOLOv5 offers a compelling combination of superior detection accuracy, lightweight design and fast detection speed [6] [7]. Batch normalization is used in YOLOv5 for each convolutional layers of the network to help achieve a faster and more stable model training, and improve detection performance [8]. The role of batch normalization is to standardize the input in each layer, which helps to reduce the internal covariate drift and improve the training speed and performance.…”

Section: Mahjong Card Identificationmentioning

confidence: 99%

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Mahjong detection based on YOLOv5

Zhang,

Zhi,

Zhou

2024

ACE

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Mahjong is a captivating game that enjoys popularity in China, Japan, and various other Asian nations. The rapid progress of artificial intelligence technology has spurred significant research attention towards achieving automated mahjong matches. Central to this effort, the precision of mahjong card recognition plays a pivotal role in determining the effectiveness of subsequent tasks.The traditional image recognition method mainly relies on the similarity measurement between the target and the template, whose accuracy and stability cannot meet the needs of the application due to changes in external factors such as the angle and lighting of the mahjong image acquisition in the actual game. In this article, a mahjong intelligent recognition method based on YOLOv5 is proposed. Specifically, this article applies image recognition technology to the AI training of national standard mahjong, and quantitatively analyzes the impact of mahjong image quality (such as shooting angle, mahjong size) on model prediction results. The results show that the proposed method based on YOLOv5 can achieve robust recognition of mahjong, which can bring some new insights to the field AI games.

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Section: Data Collection and Preprocessingmentioning

confidence: 99%