MobileNet based Chinese Chess recognition with Xavis Platform

The disadvantages of convolutional neural networks are that they cannot run on mobile devices and embedded devices due to their large memory requirements and large amounts of computation. With a small loss of accuracy, the lightweight neural network greatly reduces the number of parameters and the amount of computation compared with the ordinary convolution neural network. In order to use robot arms accurately grasp the chess pieces and place on the right place of the Chinese chess board, the chess pieces should be recognized and classified quickly in real time. In this paper, the MobileNet is embedded into the Xavis platform, which is combined with the robot arms to realize the recognition of Chinese chess. Firstly, convolutional neural network methods are introduced. Then network structure of MobileNet is analyzed, and the pattern recognition method is shown. Xavis platform with MobileNet is further investigated. And the dataset of Chinese Chess is collected. At last, experiments on the self-made Chinese chess data set show that the MobileNet has good chess classification ability. It lays a solid foundation for the grasping work of the robot arms. Next, we will further improve the method of Chinese Chess recognition with Xavis Platform and compare the proposed schedule with state-of-art methods.