How many Convolutional Layers are required for a Granite Classification Neural Network?

• Eduardo Henrique Próspero Souza IFES
• Karin Satie Komati IFES

DOI: https://doi.org/10.5753/eniac.2022.227624

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

Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. (2021). CNN variants for computer vision: History, architecture, application, challenges and future scope. Electronics, 10(20).

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.