Abstract
The mining industry plays a pivotal role in modern society, driving economic growth and sustaining our quality of life. A core challenge within this industry is the accurate identification of rock types, traditionally categorized as igneous, sedimentary, and metamorphic. Conventional methods for this classification, relying on visual assessments, spectroscopy, and chemical analyses, are not only time consuming but also often lack consistency. To address these limitations, this study explores the integration of Convolutional Neural Networks (CNNs) into the mining sector. CNNs, renowned for their image classification capabilities, have seen widespread use in various domains but are underutilized in mining. We focus on classifying rock images with a specific emphasis on granular structures, a promising application previously demonstrated. We introduce Cellular Automata (CAs) to automate grain boundary extraction within rock images. We further investigate the potential of CA-based edge detection coupled with CNNs for rock image classification. Precisely identifying grain boundaries enhances the efficiency of classification techniques, reducing the need for exhaustive iterations. Comparative analysis of the proposed model with two other CNN architectures are shown in the paper. Our proposed model shows superior result compared to the other models. Also, efficiency of CA based model is shown compared to the CNN model without using CA.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ashley, G.H., et al.: Classification and nomenclature of rock units. Bull. Geol. Soc. Am. 44(2), 423–459 (1933)
Barton, N., Lien, R., Lunde, J.: Engineering classification of rock masses for the design of tunnel support. Rock Mech. 6, 189–236 (1974)
Chen, J., Yang, T., Zhang, D., Huang, H., Tian, Y.: Deep learning based classification of rock structure of tunnel face. Geosci. Front. 12(1), 395–404 (2021)
Cheng, G., Guo, W.: Rock images classification by using deep convolution neural network. In: Journal of Physics: Conference Series. vol. 887, pp. 012089. IOP Publishing (2017)
Elngar, A.A., et al.: Image classification based on CNN: a survey. J. Cybersecur. Inf. Manag. 6(1), 18–50 (2021)
Gardner, M.: The fantastic combinations of Jhon Conway’s new solitaire game life. Sci. Am. 223, 20–123 (1970)
Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., Aryal, J.: Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens.11(2), 196 (2019)
Gorsevski, P.V., Onasch, C.M., Farver, J.R., Ye, X.: Detecting grain boundaries in deformed rocks using a cellular automata approach. Comput. Geosci. 42, 136–142 (2012)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Liu, X., Wang, H., Jing, H., Shao, A., Wang, L.: Research on intelligent identification of rock types based on faster R-CNN method. IEEE Access 8, 21804–21812 (2020)
Paty, S., Kamilya, S.: Identification of rock images in mining industry: an application of deep learning technique. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds.) Advances in Data Science and Computing Technologies. ADSC 2022. LNEE, vol. 1056. Springer, Singapore (2023). https://doi.org/10.1007/978-981-99-3656-4_24
Peacock, M.A.: Classification of igneous rock series. J. Geol. 39(1), 54–67 (1931)
Powell, C.M.: A morphological classification of rock cleavage. Tectonophysics 58(1–2), 21–34 (1979)
Zhang, Y., Li, M., Han, S.: Automatic identification and classification in lithology based on deep learning in rock images. Yanshi Xuebao/Acta Petrol. Sinica 34(2), 333–342 (2018)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Paty, S., Kamilya, S. (2024). Rock Image Classification Using CNN Assisted with Pre-processed Cellular Automata-Based Grain Detected Images. In: Dalui, M., Das, S., Formenti, E. (eds) Cellular Automata Technology. ASCAT 2024. Communications in Computer and Information Science, vol 2021. Springer, Cham. https://doi.org/10.1007/978-3-031-56943-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-56943-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56942-5
Online ISBN: 978-3-031-56943-2
eBook Packages: Computer ScienceComputer Science (R0)