How many Convolutional Layers are required for a Granite Classification Neural Network?
Resumo
O objetivo deste artigo é analisar o resultado da acurácia de classificação de tipos de granito, de acordo com a variação da quantidade de camadas convolucionais de uma CNN. A partir de uma arquitetura padrão de CNN, varia-se apenas a quantidade de camadas convolucionais, iniciando com 2 camadas até 10 camadas, incrementando uma camada a cada experimento. Foi usada uma base de dados pública, a Rock Image Datasets. Ao final, a arquitetura com 5 camadas convolucionais foi a que alcançou os melhores resultados, chegando a 99% de acurácia.Referências
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Bianconi, F., González, E., Fernández, A., and Saetta, S. A. (2012). Automatic classification of granite tiles through colour and texture features. Expert Systems with Applications, 39(12):11212-11218.
Khan, A., Sohail, A., Zahoora, U., and Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial intelligence review, 53(8):5455-5516.
Labatut, V. and Cherifi, H. (2012). Accuracy measures for the comparison of classifiers. arXiv preprint arXiv:1207.3790.
Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems.
O'Shea, K. and Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
Pascual, A. D., McIsaac, K. M., and Osinski, G. (2021). Deep learning of rock images for intelligent lithology identification. Preprints.
Pascual, A. D. P., Shu, L., Szoke-Sieswerda, J., McIsaac, K., and Osinski, G. (2019). Towards natural scene rock image classification with convolutional neural networks. In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), pages 1-4. IEEE.
Ran, X., Xue, L., Zhang, Y., Liu, Z., Sang, X., and He, J. (2019). Rock classification from field image patches analyzed using a deep convolutional neural network. Mathematics, 7(8):755.
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Chen, X., and Wang, X. (2020). A comprehensive survey of neural architecture search: Challenges and solutions. arXiv preprint arXiv:2006.02903.
Shu, L., McIsaac, K., Osinski, G. R., and Francis, R. (2017). Unsupervised feature learning for autonomous rock image classification. Computers & Geosciences, 106:10-17.
Xu, Z., Ma, W., Lin, P., and Hua, Y. (2022). Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection. Journal of Rock Mechanics and Geotechnical Engineering, 14(4):1140-1152.
Bianconi, F., González, E., Fernández, A., and Saetta, S. A. (2012). Automatic classification of granite tiles through colour and texture features. Expert Systems with Applications, 39(12):11212-11218.
Khan, A., Sohail, A., Zahoora, U., and Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial intelligence review, 53(8):5455-5516.
Labatut, V. and Cherifi, H. (2012). Accuracy measures for the comparison of classifiers. arXiv preprint arXiv:1207.3790.
Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems.
O'Shea, K. and Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
Pascual, A. D., McIsaac, K. M., and Osinski, G. (2021). Deep learning of rock images for intelligent lithology identification. Preprints.
Pascual, A. D. P., Shu, L., Szoke-Sieswerda, J., McIsaac, K., and Osinski, G. (2019). Towards natural scene rock image classification with convolutional neural networks. In 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), pages 1-4. IEEE.
Ran, X., Xue, L., Zhang, Y., Liu, Z., Sang, X., and He, J. (2019). Rock classification from field image patches analyzed using a deep convolutional neural network. Mathematics, 7(8):755.
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Chen, X., and Wang, X. (2020). A comprehensive survey of neural architecture search: Challenges and solutions. arXiv preprint arXiv:2006.02903.
Shu, L., McIsaac, K., Osinski, G. R., and Francis, R. (2017). Unsupervised feature learning for autonomous rock image classification. Computers & Geosciences, 106:10-17.
Xu, Z., Ma, W., Lin, P., and Hua, Y. (2022). Deep learning of rock microscopic images for intelligent lithology identification: Neural network comparison and selection. Journal of Rock Mechanics and Geotechnical Engineering, 14(4):1140-1152.
Publicado
28/11/2022
Como Citar
SOUZA, Eduardo Henrique Próspero; KOMATI, Karin Satie.
How many Convolutional Layers are required for a Granite Classification Neural Network?. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 798-808.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2022.227624.
