Abstract
Based on the tedious process of using a carbon-sulfur analyzer to detect the carbon grade of graphite in graphite mining production, this paper proposes a graphite carbon grade image recognition and classification method based on multi-scale feature fusion. The experiment preprocesses the images and constructs a residual network model that combines pyramid convolution (PyConv) and spatial attention mechanism (SAM). This model enhances the extraction of both global and local feature information from graphite images. Transfer learning is introduced by using pre-trained weights to accelerate the convergence of the model, achieving efficient and accurate recognition and classification of graphite carbon grade with an accuracy of 92.5%, surpassing traditional machine learning methods using single features. The experimental results demonstrate that the neural network model constructed in this paper effectively extracts texture and color features from graphite images, improving the accuracy of graphite image classification and recognition. The model exhibits good robustness and provides valuable insights for practical graphite mining production.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xie, J.: Exploration of the method for determining the fixed carbon content in carbonate graphite ore based on high-frequency infrared. Gansu Sci. Technol. 39(01), 18–20 (2023)
Liu, Y., Xiang, L., Dong, A., et al.: Study on the method of measuring fixed carbon in graphite by high-frequency infrared carbon-sulfur analyzer. Chem. Eng. Des. Commun. 47(11), 44–45 (2021)
Chai, F.: Exploration and practice of intelligent mine construction in Zhengzhuang coal mine. Shandong Coal Sci. Technol. 40(12), 25–27+34 (2022)
Pinto, D.L., et al. Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms. Livestock Sci. 267 (2023)
Baigts, A.D., RamÃrez, RM., Rosas, R.R.: Monitoring of the dehydration process of apple snacks with visual feature extraction and image processing techniques. Appl. Sci. 12(21), 11269–11269 (2022)
Singh, N., Singh, T.N., Tiwary, A., Sarkar, K.: Textural identification of basaltic rock mass using image processing and neural network. Comput. Geosci. 14, 301–310 (2010)
Deepa, S., Umarani, R.: Steganalysis on images using SVM with selected hybrid features of Gini index feature selection algorithm. Int. J. Adv. Res. Comput. Sci. 8(5), 1503–1509 (2017)
Yang, L., Bai, Z., Kou, Y.G.: Analysis of the loss of civil aviation customers by random forest algorithm based on RFM model. Comput. Modernization 1, 100–104 (2021)
Guettari, N., Capelle-Laizé, A.S., Carr, P.: Blind image steganalysis based on evidential K-Nearest neighbors. In: Proceedings of the 2016 IEEE International Conference on Image Processing, 2742–2746 (2016)
Dong, W.K., Min, S.K., Lee, J., et al.: Adaptive learning rate backpropagation neural network algorithm based on the minimization of mean square deviation for impulsive noises. IEEE Access 8, 98018–98026 (2020)
Zhou, N., Ouyang, X.Y.: Development of convolutional neural networks. J. Liaoning Univ. Sci. Technol. 44(05), 349–356 (2021)
Yan, C., Wang, C.: Development and application of convolutional neural network models. J. Comput. Sci. Explor. 15(01), 27–46 (2021)
Chen, W., et al.: Rock image classification using deep residual neural network with transfer learning. Front. Earth Sci. (2023)
Cheng, G., Guo, W., Fan, P.: Rock image classification based on convolutional neural network. J. Xi’an Shiyou Univ. (Nat. Sci. Edit.) 32(04), 116–122 (2017)
Bai, L., Yao, Y., Li, S., Xu, D., Wei, X.: Mineral composition analysis of rock images based on deep learning feature extraction. China Min. 27(07), 178–182 (2018)
Bai, L., Wei, X., Liu, Y., Wu, C., Chen, L.: Rock thin section image recognition based on VGG model. Geol. Bull. 38(12), 2053–2058 (2019)
Liu, X., Wang, H., Wang, L.: Intelligent recognition of rock types based on Faster R-CNN method. Mod. Min. 35(05), 60–64 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Huang, X., Shi, H., Liu, Y., Lu, H. (2024). Image Classification Algorithm for Graphite Ore Carbon Grade Based on Multi-scale Feature Fusion. In: Wu, C., Chen, X., Feng, J., Wu, Z. (eds) Mobile Networks and Management. MONAMI 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-55471-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-55471-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-55470-4
Online ISBN: 978-3-031-55471-1
eBook Packages: Computer ScienceComputer Science (R0)