An Intelligent Chess Piece Detection Tool
Resumo
Chess is one of the most researched domains in the annals of artificial intelligence. The main objective of this research is to develop a platform that can determine piece positioning during chess games. Digital image processing methods and real-time object detection (YOLO version 4) algorithms were used during computational development. The problem entails analyzing images captured during a chess game and determining the location of each square on the board, as well as the position of each piece in play. This procedure is repeated at each game turn, enabling the developed system to save and watch all piece moves during a game. The obtained results demonstrate the system’s reliability and feasibility.Referências
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Leonard, M., Westra, S., Phatak, A., Lambert, M., van den Hurk, B., McInnes, K., Risbey, J., Schuster, S., Jakob, D., and Stafford-Smith, M. (2014). A compound event frame work for understanding extreme impacts. Wiley Interdisciplinary Reviews: Climate Change, 5(1):113–128.
Menezes, R., de Miranda, A., and Maia, H. (2022). Pymicetracking: An open-source toolbox for real-time behavioral neuroscience experiments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21459–21465.
Pan, W. D., Dong, Y., and Wu, D. (2018). Classification of malaria-infected cells using deep convolutional neural networks. Machine learning: advanced techniques and emerging applications, 159.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015). You only look once: Unified, real-time object detection. arxiv 2015. arXiv preprint arXiv:1506.02640.
Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252.
Srinivas, S., Sarvadevabhatla, R. K., Mopuri, K. R., Prabhu, N., Kruthiventi, S. S., and Babu, R. V. (2017). An introduction to deep convolutional neural nets for computer vision. In Deep Learning for Medical Image Analysis, pages 25–52. Elsevier.
de Menezes, R. S. T., Cordeiro, A. M., Magalhães, R. M., and Maia, H. (2021). Classification of paintings authorship using convolutional neural network. Sociedade Brasileira de Inteligência Computacional.
de Menezes, R. S. T., de Azevedo Lima, L., Santana, O., Henriques-Alves, A. M., Santa Cruz, R. M., and Maia, H. (2018). Classification of mice head orientation using support vector machine and histogram of oriented gradients features. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–6. IEEE.
de Menezes, R. S. T., Luiz, J. V. A., Henrique-Alves, A. M., Santa Cruz, R. M., and Maia, H. (2020). Mice tracking using the yolo algorithm. In Anais do XLVII Seminário Integrado de Software e Hardware, pages 162–173. SBC.
Deng, L., Hinton, G., and Kingsbury, B. (2013). New types of deep neural network learning for speech recognition and related applications: An overview. In 2013 IEEE international conference on acoustics, speech and signal processing, pages 8599–8603. IEEE.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639):115–118.
Girshick, R. (2015). Fast r-cnn. arxiv 2015. arXiv preprint arXiv:1504.08083.
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2013). Rich feature hierarchies for accurate object detection and semantic segmentation. corr, abs/1311.2524. arXiv preprint arXiv:1311.2524.
Jayaraman, V., Chandrasekhar, M., and Rao, U. (1997). Managing the natural disasters from space technology inputs. Acta Astronautica, 40(2-8):291–325.
Kogan, F. N. (1997). Global drought watch from space. Bulletin of the American Meteorological Society, 78(4):621–636.
Kriegeskorte, N. (2015). Deep neural networks: a new framework for modelling biological vision and brain information processing. biorxiv, page 029876.
Leonard, M., Westra, S., Phatak, A., Lambert, M., van den Hurk, B., McInnes, K., Risbey, J., Schuster, S., Jakob, D., and Stafford-Smith, M. (2014). A compound event frame work for understanding extreme impacts. Wiley Interdisciplinary Reviews: Climate Change, 5(1):113–128.
Menezes, R., de Miranda, A., and Maia, H. (2022). Pymicetracking: An open-source toolbox for real-time behavioral neuroscience experiments. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21459–21465.
Pan, W. D., Dong, Y., and Wu, D. (2018). Classification of malaria-infected cells using deep convolutional neural networks. Machine learning: advanced techniques and emerging applications, 159.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2015). You only look once: Unified, real-time object detection. arxiv 2015. arXiv preprint arXiv:1506.02640.
Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252.
Srinivas, S., Sarvadevabhatla, R. K., Mopuri, K. R., Prabhu, N., Kruthiventi, S. S., and Babu, R. V. (2017). An introduction to deep convolutional neural nets for computer vision. In Deep Learning for Medical Image Analysis, pages 25–52. Elsevier.
Publicado
06/08/2023
Como Citar
MENEZES, Richardson; MAIA, Helton.
An Intelligent Chess Piece Detection Tool. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 50. , 2023, João Pessoa/PB.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 60-70.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2023.229800.
