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基于改进的Pix2pix的Go-Game图像识别

Yanxia Zheng1, Xiyuan Qian1

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

Journal of imaging
|December 22, 2023
PubMed
概括

这项研究介绍了一种改进的pix2pix模型用于Go游戏的图像识别,提高了准确性和概括性. 这种新方法显著优于现有自动化围棋板分析技术.

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 图像识别 图像识别

背景情况:

  • 传统的围棋游戏得分依赖于手动计数,这是低效的,容易出错.
  • 现有的Go图像识别自动化方法普遍性不佳,需要改进精度.

研究的目的:

  • 开发一种新的Go游戏图像识别系统,克服手动计数和当前自动化方法的局限性.
  • 为了提高Go游戏图像识别模型的准确性和概括能力.

主要方法:

  • 为Go游戏的图像识别提出了改进的pix2pix模型.
  • 整合了道协调混合注意力 (CCMA) 机制以改善特征学习.
  • 引入了一个深度扩展卷积 (DDC) 模块,以捕获远距离的上下文信息.

主要成果:

  • 与DenseNet,VGG-16和Yolo v5.5相比,提出的方法显示出更高的性能.
  • 该模型实现了超过99.99%的平均准确率.
  • 观察到一般化能力和准确性的显著改善.

结论:

  • 改进的pix2pix模型为Go游戏图像识别提供了一个高度准确和可通用的解决方案.
关键词:
在CCMA中,CCMA是CCMA,CCMA是CCMA.DDC DDC DDC 的意思是图像识别功能 图像识别功能像素2pixxxx 在线观看

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  • 这种方法有效地解决了与自动化Go板分析相关的挑战.