文献类型：期刊文献

英文题名：Go-Game Image Recognition Based on Improved Pix2pix

作者：Zheng, Yanxia[1];Qian, Xiyuan[1]

机构：[1]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China

年份：2023

卷号：9

期号：12

外文期刊名：JOURNAL OF IMAGING

收录：EI(收录号：20235215270377);WOS:【ESCI(收录号:WOS:001131017600001)】；

基金：The authors would like to thank all who contributed to this study.

语种：英文

外文关键词：pix2pix; image recognition; CCMA; DDC

摘要：Go is a game that can be won or lost based on the number of intersections surrounded by black or white pieces. The traditional method is a manual counting method, which is time-consuming and error-prone. In addition, the generalization of the current Go-image-recognition methods is poor, and accuracy needs to be further improved. To solve these problems, a Go-game image recognition based on an improved pix2pix was proposed. Firstly, a channel-coordinate mixed-attention (CCMA) mechanism was designed by combining channel attention and coordinate attention effectively; therefore, the model could learn the target feature information. Secondly, in order to obtain the long-distance contextual information, a deep dilated-convolution (DDC) module was proposed, which densely linked the dilated convolution with different dilated rates. The experimental results showed that compared with other existing Go-image-recognition methods, such as DenseNet, VGG-16, and Yolo v5, the proposed method could effectively improve the generalization ability and accuracy of a Go-image-recognition model, and the average accuracy rate was over 99.99%.