Weiqi referee system based on MLP neural network and computer vision

Abstract

The invention relates to a Weiqi referee system based on an MLP neural network and computer vision. The Weiqi referee system comprises an image normalization processing module, an MLP neural network module and a Weiqi referee algorithm module. The image normalization processing module is used for preprocessing an image by means of channel transformation, image cutting, light equalization processing, corner detection and the like, so that subsequent recognition is facilitated. The MLP neural network module comprises a chessboard recognition model and a chess piece recognition model and is usedfor recognizing the positions of the chessboard and the black and white chess pieces and storing the information of the chessboard and the black and white chess pieces in a TXT file. The later-stage Weiqi referee algorithm module is used for judging the winning or losing of the chess game, obtaining the winning or losing state of the black and white chess according to an algorithm by reading the state and position information of the black and white chess in the TXT file, converting a result into an SGF (universal go chess manual) picture and displaying the SGF picture to a user.

Description

technical field [0001] The invention belongs to MLP neural network and image recognition technology, and specifically relates to image acquisition and image processing technology. Background technique [0002] With the development of artificial intelligence, the application of deep learning and image recognition technology is more extensive. As the starting point of deep learning, MLP neural network has certain advantages in dealing with classification problems. In the field of Go, due to holding a large-scale Go game, a large number of Go referees need to be hired, the cost is high, and the judgment speed is slow, and judgment errors may also be made. Therefore, many people began to study algorithms, through image acquisition, image processing, and then through the Go discrimination algorithm to determine the outcome. Among the many methods, a fixed camera is used, which cannot be moved. Finding a nearly perfect shooting angle has high requirements for hardware equipment a...

AI Technical Summary

Problems solved by technology

However, the effect that people want is still not achieved. The biggest problem is that there are many unforeseen influencing factors, so a specific method cannot be applied to all situations.

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