Abstract
A deep learning method based on convolutional neural network is introduced into the human–machine game of Chinese chess to detect and recognite pieces, which uses the faster R-CNN target detection architecture and the GoogLeNet convolutional neural network. Based on the shape features of pieces, an anchor mechanism is designed to detect pieces. The method of extracting eight-direction features and rotating invariant HOG features are used to train the ResNet convolutional neural network, which can improve the problem of fewer types of training-intensive chess pieces, single rotating pictures, etc. Experimental results show that the proposed convolutional neural network for chess detection and recognition can greatly enhance the detection of environmental adaptability, robustness, and improve the detection speed and the recognition accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dang, H.S., Zhang, C., Pang, Y., et al.: Research on chess rapid recognition and positioning system based on ORB algorithm. Sci. Technol. Eng. 17(7), 52–57 (2017)
Yang, W., Jin, L., Xie, Z., et al.: Improved deep convolutional neural network for online handwritten Chinese character recognition using domain-specific knowledge. Guangzhou 15(6), 551–555 (2015)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: International Conference on Neural Information Processing Systems. MIT Press, pp. 91–99, 2015
Sabokrou, M., Fayyaz, M., Fathy, M., et al.: Deep-anomaly: fully convolutional neural network for fast anomaly detection in crowded scenes. Comput. Vis. Image Underst. (2018)
Szegedy, C., Reed, S., Erhan, D., Anguelov, D.: Scalable, high-quality object detection. arXiv, 2015(2), 1412–1441 (2015)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, pp. 1–9, 2015
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. 2015, 448–456 (2015)
Zhong, Z., Jin, L., Xie, Z.: High performance offline handwritten Chinese character recognition using GoogLeNet and directional feature maps. In: International Conference on Document Analysis and Recognition, pp. 846–850. IEEE, 2015
Acknowledgements
The paper was supported by the Natural Science Foundation of China (61672396, 61373110).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, C., Chen, G. (2020). Research on Chinese Chess Detection and Recognition Based on Convolutional Neural Network. In: Jain, V., Patnaik, S., Popențiu Vlădicescu, F., Sethi, I. (eds) Recent Trends in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 1006. Springer, Singapore. https://doi.org/10.1007/978-981-13-9406-5_57
Download citation
DOI: https://doi.org/10.1007/978-981-13-9406-5_57
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9405-8
Online ISBN: 978-981-13-9406-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)