Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

A method of go recognition based on Deep Learning

Authors
Heng Ran, Pengyun Song, Yanghui Liu, Lei Yu, Hang Zhou, Yinrui Zhang
Corresponding Author
Heng Ran
Available Online March 2018.
DOI
https://doi.org/10.2991/mecae-18.2018.45How to use a DOI?
Keywords
Deep learning, go, image processing.
Abstract
In order to obtain the image information of go, we use the traditional method to process the image, but the traditional method has great limitation. The accuracy is low, and the external factors can easily affect the processing result. In order to solve the limitation of traditional image processing method, we will introduce and study a new image processing method-depth learning. First, we prepare a picture sample of go, and then we use two methods to identify the image of go. Finally, the recognition results of the traditional method and the recognition results of the depth learning are compared. The results show that the accuracy of the depth learning method is improved by 17.1% compared with the traditional method.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Part of series
Advances in Engineering Research
Publication Date
March 2018
ISBN
978-94-6252-493-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecae-18.2018.45How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Heng Ran
AU  - Pengyun Song
AU  - Yanghui Liu
AU  - Lei Yu
AU  - Hang Zhou
AU  - Yinrui Zhang
PY  - 2018/03
DA  - 2018/03
TI  - A method of go recognition based on Deep Learning
BT  - 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.45
DO  - https://doi.org/10.2991/mecae-18.2018.45
ID  - Ran2018/03
ER  -