MiNet: A Convolutional Neural Network for Identifying and Categorising Minerals

Millicent Akotam Agangiba, Emmanuel Brempong Asiedu, Derrick Aikins

Abstract


Identification of minerals in the field is a task that is wrought with many challenges. Traditional approaches are prone to errors where there is no enough experience and expertise. Several existing techniques mainly make use of features of the minerals under a microscope and tend to favour a manual feature extraction pipeline Deep learning methods can help overcome some of these hurdles and provide simple and effective ways to identify minerals. In this paper, we present an algorithm for identifying minerals from hand specimen images. Using a Convolutional Neural Network (CNN), we develop a single-label image classification model to identify and categorise seven classes of minerals. Experiments conducted using real-world datasets show that the model achieves an accuracy of 94.2 per cent.


Keywords


Minerals, Physical Properties, Convolutional Neural Network (CNN)

Full Text:

PDF

References


Anon. (2014), “The Role of Mining in National Economies”, www.icmm.com, Accessed: September 30, 2018.

Anon. (2016), “Factoid on the Industry’s Performance”,www. ghanachamberofmines.org, Accessed: September 10, 2018.

Anthony, W. J., Bideaux, A. R. and Bladh, W. K. , eds. (2018), “Handbook of Mineralogy”, www.handbookofmineralogy.com. Accessed: November 4, 2018.

Ault, C. A. Jr. (1998), “Criteria of excellence for geological inquiry: The necessity of ambiguity”, Journal of Research in Science Teaching, Vol. 35, pp. 189 – 212.

Baykan, N. A.andYılmaz, N. (2011), “A Mineral Classification System with Multiple Artificial Neural Network Using k-Fold Cross Validation”, Mathematical and Computational Applications, Vol. 16, No. 1, pp. 22-30.

Bonewitz, R. L. (2012), Rocks and Minerals, Dorling Kindersley Publishing, New York, 354 pp.

Chollet, F. (2017), Deep Learning with Python, Manning Publications, New York, 384pp.

Deng, J., Dong, W., Socher, R., Li, L. J., Li, K. and Fei-Fei, L. (2009), “Imagenet: A Large-Scale Hierarchical Image Database”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255.

Egger, A. E. (2005), “Defining Minerals”, Vision Learning, Vol. EAS-2, No. 6.

Ford, D. (2005), “The challenges of observing geologically: Third grades descriptions of rock and mineral properties”, Science Education, Vol.89, pp.276–295.

Goodfellow, I., Benjio, Y. and Courvile, A. (2016), Deep Learning, The MIT Press, Massachusetts, 800pp.

Huang, G., Liu, Z., Maaten, L. v. d., Weinberger, K. Q. (2017), “Densely Connected Convolutional Networks”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700-4708.

Izadi, H. and Sadri, J. (2018), “Application of Pattern Recognition in Mineral Segmentation and Identification”, International Conference on Pattern Recognition and Artificial Intelligence, Montreal, 14-17 May.

Khun, M. and Johnson, K. (2013), Applied Predictive Modeling, Springer, Basel, 600pp.

Kingma, D. P. and Ba, J. (2014), “Adam: A method for Stochastic Optimization”, arXiv preprint arXiv:1412.6980.

Loshchilov, I. and Hutter, F. (2019), “Decoupled Weight Decay Regularization”, arXiv preprint arXiv:1711.05101v3.

King, H. M. (2019), “What are Minerals?”, www.geology.com. Accessed: March 5, 2019.

LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. (1998), “Gradient-based learning applied to document recognition”, Proceedings of the IEEE, 86(11), pp. 2278-2324.

LeCun, Y., Bengio, Y. and Hinton, G. (2015), “Deep Learning”, Nature, Vol. 521, pp. 436 – 444.

McMahon, G. and Moreira, S. (2014), “The Contribution of the Mining Sector to Socioeconomic and Human Development”, Extractive Industries for Development Series, No. 30.

O’Shea, K. and Nash, R. (2015), “An Introduction to Convolutional Neural Networks”, arXiv preprint arXiv:1511.08458v2.

Rafferty, J. P., ed. (2012), Geology: Landforms, Minerals and Rocks, Rosen Educational Services, New York, 358 pp.

Thompson, S., Fueten, F. and Bockus,D. (2001), “Mineral identification using artificial neural networks and the rotating polarizer stage”, Computers and Geosciences, Vol. 27, pp. 1081-1089.

Yao, K., Pradhan, B. and Idrees, M. O. (2017), “Identification of Rocks and Their Quartz Content in Gua Musang Goldfield Using Advanced Spaceborne Thermal Emission and Reflection Radiometer Imagery”, Journal of Sensors, Vol. 2017, Article Id 6794095, 8 pp.


Refbacks

  • There are currently no refbacks.