Ferrous Image Classification Based on YOLOv8
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Ferrous Image Classification Based on YOLOv8

Ang Li 1, Xiaojun Qi 2* Zhebin Yu 3, Jialin Wei 4, Wei Liu 5, Wei Su 6, Zongfa Li 7
1 China National Gold Group Geology Co., Ltd.
2 China National Gold Group Geology Co., Ltd.
3 China National Gold Group Geology Co., Ltd.
4 China National Gold Group Geology Co., Ltd.
5 China National Gold Group Geology Co., Ltd.
6 China National Gold Group Geology Co., Ltd.
7 China National Gold Group Geology Co., Ltd.
*Corresponding author: slm0718m@163.com
Published on 11 July 2025
Journal Cover
ACE Vol.173
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-231-7
ISBN (Online): 978-1-80590-232-4
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Abstract

This paper, we propose a new mineral image classification method using YOLOv8 model enhanced by visual attention mechanism. The integration of attention blocks improves the model's ability to focus on relevant features, thereby reducing misclassification and improving accuracy, especially in complex and noisy environments. Experimental results using iron ore with different densities show that the attention-enhanced YOLOv8 outperforms traditional methods in ferrous iron type classification based on density, ash and microfraction. The proposed method significantly improves the efficiency of feature extraction and processing, which provides a promising solution for intelligent ore sorting in industrial applications.

Keywords:

YOLOv8, Visual Attention Mechanism, Mineral Image Classification, Deep Learning, Intelligent Ore Sorting.

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Li,A.;Qi,X.;Yu,Z.;Wei,J.;Liu,W.;Su,W.;Li,Z. (2025). Ferrous Image Classification Based on YOLOv8. Applied and Computational Engineering,173,43-49.

References

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[3]. M. Massinaei, A. Jahedsaravani, E. Taheri, and J. Khalilpour, ‘‘Machine vision based monitoring and analysis of a coal column flotation circuit, ’’ Powder Technol., vol. 343, pp. 330–341, Feb. 2019, doi: 10.1016/j.powtec.2018.11.056.

[4]. A. D. Gordon, L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, vol. 40, no. 3. Belmont, CA, USA: Wadsworth International Group, 1984.

[5]. T. Cover and P. Hart, ‘‘Nearest neighbor pattern classification, ’’ IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, Jan. 1967, doi: 10.1109/TIT.1967.1053964.

[6]. Y. Liu, Z. Zhang, X. Liu, L. Wang, and X. Xia, ‘‘Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size, ’’ Minerals Eng., 2021, Art. no. 107020, doi: 10.1016/j.mineng.2021.107020.

[7]. Z. Zhang, Y. Liu, Q. Hu, Z. Zhang, and Y. Liu, ‘‘Competitive votingbased multi-class prediction for ore selection, ’’ in Proc. IEEE 16th Int. Conf. Autom. Sci. Eng. (CASE), Aug. 2020, pp. 514–519, doi: 10.1109/CASE48305.2020.9217017.

[8]. Wu B, Cai Z, Wu W, et al. AoI-aware resource management for smart health via deep reinforcement learning [J]. IEEE Access, 2023.

References

[1]. Sun Y, Ortiz J. An ai-based system utilizing iot-enabled ambient sensors and llms for complex activity tracking [J]. arXiv preprint arXiv: 2407.02606, 2024.

[2]. C. Robben, P. Condori, A. Pinto, R. Machaca, and A. Takala, ‘‘X-raytransmission based ore sorting at the san rafael tin mine, ’’ Minerals Eng., vol. 145, Jan. 2020, Art. no. 105870, doi: 10.1016/j.mineng.2019.105870.

[3]. M. Massinaei, A. Jahedsaravani, E. Taheri, and J. Khalilpour, ‘‘Machine vision based monitoring and analysis of a coal column flotation circuit, ’’ Powder Technol., vol. 343, pp. 330–341, Feb. 2019, doi: 10.1016/j.powtec.2018.11.056.

[4]. A. D. Gordon, L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, vol. 40, no. 3. Belmont, CA, USA: Wadsworth International Group, 1984.

[5]. T. Cover and P. Hart, ‘‘Nearest neighbor pattern classification, ’’ IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, Jan. 1967, doi: 10.1109/TIT.1967.1053964.

[6]. Y. Liu, Z. Zhang, X. Liu, L. Wang, and X. Xia, ‘‘Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size, ’’ Minerals Eng., 2021, Art. no. 107020, doi: 10.1016/j.mineng.2021.107020.

[7]. Z. Zhang, Y. Liu, Q. Hu, Z. Zhang, and Y. Liu, ‘‘Competitive votingbased multi-class prediction for ore selection, ’’ in Proc. IEEE 16th Int. Conf. Autom. Sci. Eng. (CASE), Aug. 2020, pp. 514–519, doi: 10.1109/CASE48305.2020.9217017.

[8]. Wu B, Cai Z, Wu W, et al. AoI-aware resource management for smart health via deep reinforcement learning [J]. IEEE Access, 2023.

Cite this article

Li,A.;Qi,X.;Yu,Z.;Wei,J.;Liu,W.;Su,W.;Li,Z. (2025). Ferrous Image Classification Based on YOLOv8. Applied and Computational Engineering,173,43-49.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 7th International Conference on Computing and Data Science

ISBN: 978-1-80590-231-7(Print) / 978-1-80590-232-4(Online)
Editor: Marwan Omar
Conference website: https://2025.confcds.org/
Conference date: 25 September 2025
Series: Applied and Computational Engineering
Volume number: Vol.173
ISSN: 2755-2721(Print) / 2755-273X(Online)

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