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Go-Game Image Recognition Based on Improved Pix2pix.

Yanxia Zheng1, Xiyuan Qian1

  • 1School of Mathematics, East China University of Science and Technology, Shanghai 200237, China.

Journal of Imaging
|December 22, 2023
PubMed
Summary

This study introduces an improved pix2pix model for Go game image recognition, enhancing accuracy and generalization. The new method significantly outperforms existing techniques for automated Go board analysis.

Keywords:
CCMADDCimage recognitionpix2pix

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Recognition

Background:

  • Traditional Go game scoring relies on manual counting, which is inefficient and prone to errors.
  • Existing automated Go image recognition methods suffer from poor generalization and require accuracy improvements.

Purpose of the Study:

  • To develop a novel Go-game image recognition system that overcomes the limitations of manual counting and current automated methods.
  • To enhance the accuracy and generalization capabilities of Go game image recognition models.

Main Methods:

  • An improved pix2pix model was proposed for Go game image recognition.
  • A channel-coordinate mixed-attention (CCMA) mechanism was integrated to improve feature learning.
  • A deep dilated-convolution (DDC) module was introduced to capture long-distance contextual information.

Main Results:

  • The proposed method demonstrated superior performance compared to DenseNet, VGG-16, and Yolo v5.
  • The model achieved an average accuracy rate exceeding 99.99%.
  • Significant improvements in generalization ability and accuracy were observed.

Conclusions:

  • The improved pix2pix model offers a highly accurate and generalizable solution for Go game image recognition.
  • This approach effectively addresses the challenges associated with automated Go board analysis.