A Low-Complexity Algorithm for Estimation of The P80 Rock Granulometry Indicator Based on Digital Image Processing

DOI: https://doi.org/10.52549/ijeei.v14i1.7180
Published: 11-03-2026 • Issue: Vol. 14 No. 1: March 2026 • Section: Regular paper

Abstract

This work presents an algorithm that automatically measures rock size distribution during primary crushing in mining, continuously monitoring without interrupting production or causing issues in subsequent crushing stages due to oversized rocks. Existing commercial solutions demand significant investment and infrastructure, making them unfeasible for small mining operations. As a result, these companies typically halt production and send samples to a laboratory to determine the P80 granulometric factor. Most current research focuses on edge detection and rock segmentation, often overlooking the estimation of granulometric parameters. The proposed algorithm uses low-complexity image processing techniques that can run on a small-board computer. Images are captured by a camera positioned above a conveyor belt within an image acquisition enclosure and processed using watershed segmentation and morphological operations like erosion and dilation. The P80 value is estimated through the Rosin-Rammler linearization model. Results show 89% accuracy compared to laboratory measurements and a 91.3% success rate.

References

Navarro, P., (n.d.). Conminución y Separación de Tamaños [PowerPoint Slides]. Studocu. https://www.studocu.com/cl/document/universidad-de-playa-ancha-de-ciencias-de-la-educacion/procesos-industriales-ii/conminucion-y-separacion-de-tamanos-proceso-minero/16731241

Split Engineering. (n.d.). Split-Online software and systems. Retrieved August 10, 2022, from https://www.spliteng.com/products/split-online-systems/

Esco, (n.d.). BeltMetrics. Retrieved August 10, 2022, from https://www.esco.weir/motion-metrics/beltmetrics/

Innovative Machine Vision (n.d.). 3DPM: Automated Particle Size Distribution Measurement using 3D Imaging. Retrieved August 12, 2022, from https://www.innovativemachinevision.com/3dpm/

Cheng, G., & Guo, W. (2017). Rock images classification by using deep convolution neural network. Journal of Physics: Conference Series, 887, 012089. https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012089

Li, H., Asbjörnsson, G., & Lindqvist, M. (2021). Image process of rock size distribution using DexiNed-based neural network. Minerals, 11(7), 736. https://doi.org/10.3390/min11070736

Soria, X., Riba, E., & Sappa, A. (2020). Dense Extreme Inception Network: Towards a robust CNN model for edge detection. arXiv. https://doi.org/10.48550/arXiv.1909.01955

La Serna, N., & Román, U. (2009). Técnicas de segmentación en el procesamiento digital de imágenes. Revista de Investigación de Sistemas e Informática, 6(2), 9–16. https://revistasinvestigacion.unmsm.edu.pe/index.php/sistem/article/view/3299

Brondolo, F., & Beaussant, S. (2025). DINOv2 rocks geological image analysis: Classification, segmentation, and interpretability. Journal of Rock Mechanics and Geotechnical Engineering, 17, 6853–6867. https://doi.org/10.1016/j.jrmge.2025.01.057

Cañas, J., (n.d.). Análisis de tamaño de partículas por tamizado en agregado fino y grueso y determinación de material más fino que el tamiz No. 200 (75um) en agregado de mineral por lavado. Retrieved January 20, 2023, from https://www.uca.edu.sv/mecanica-estructural/materias/materialesCostruccion/guiasLab/ensayoAgregados/GRANULOMETRIA.pdf

Zumaran, D. (2017). Evaluación de la influencia de variables en la distribución granulométrica de los productos del molino de bolas de minerales a través de diseños experimentales [Undergraduate thesis, Universidad Nacional de San Agustín]. Repositorio académico UNSA. http://repositorio.unsa.edu.pe/bitstream/handle/UNSA/3214/IMzufedm.pdf?sequence=1&isAllowed=y

Universidad Politécnica de Madrid, (n.d.). Determinación de la granulometría de las partículas. Método del tamizado. Retrieved August 7, 2024, from https://www2.caminos.upm.es/departamentos/ict/lcweb/ensayos_aridos/granulometria.html

Raspberry Pi. (n.d.). Sobre módulos de cámaras. Retrieved November 5, 2022, from https://www.raspberrypi.com/documentation/accessories/camera.html

Calixto Muñoz, S. A., Del Carpio Damián, C. C., Eyzaguirre Andrade, R. M. A. & Kemper Vásquez, G. L. (2023). Equipo electrónico de adquisición de imágenes digitales para la obtención de parámetros geométricos de una antena tipo Yagi y logarítmica (Patent No. PE20231925Z). Espacenet. https://worldwide.espacenet.com/patent/search/family/093432823/publication/PE20231925Z

Cardenas, E., Tabory, E., Sanchez, A., & Kemper, G. (2024). An electronic equipment for marbling meat grade detection based on digital image processing and support vector machine. *Journal of the Saudi Society of Agricultural Sciences*. Advance online publication. https://doi.org/10.1016/j.jssas.2024.05.001

Domínguez, H. (2018). Implementación de un mecanismo de regularización basado en filtros bilaterales para algoritmos de segmentación de objetos en movimiento [Undergraduate thesis, Universidad Politécnica de Madrid]. Repositorio académico UPM. https://oa.upm.es/51983/1/PFC_HENAR_DOMINGUEZ_ELVIRA_2018.pdf

Singh, T. R., Roy, S., Singh, O. I., Sinam, T., & Singh, K. M. (2012). A new local adaptive thresholding technique in binarization. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1201.5227

Blanco, A., Sánchez, J., Del Carpio, C., Dianderas, E., & Kemper, G. (2024). Identification of Peruvian timber species based on image processing and convolutional neural networks. *International Journal of Applied Science and Engineering, 21*(1), 2023274. https://doi.org/10.6703/IJASE.202403_21(1).001

Borgefors, G. (1986). Distance transformations in digital images. Computer Vision Graphics and Image Processing, 34(3), 344–371. https://doi.org/10.1016/s0734-189x(86)80047-0

Kornilov, A., Safonov, I., & Yakimchuk, I. (2022). A review of watershed implementations for segmentation of volumetric images. Journal of Imaging, 8(5), 127. https://doi.org/10.3390/jimaging8050127

MarJent (2023). Reporte de ensayo L007-007. MarJent

Allen, T. (1981). Particle size, shape and distribution BT - Particle Size Measurement, Ed. Boston, MA: Springer US, pp. 103–164. doi: 10.1007/978-1-4899-3063-7_4.

Quijano, V. (2018). Modelación de la distribución de tamaño de productos de molienda de mena laterítica [Undergraduate thesis, Instituto Superior Minero Metalúrgico]. Repositorio académico ISMM. https://ninive.ismm.edu.cu/bitstream/handle/123456789/1699/QuijanoArteaga.pdf?sequence=1&isAllowed=y

How to Cite

[1]
J. Aybar, P. Tasayco, and G. Kemper, “A Low-Complexity Algorithm for Estimation of The P80 Rock Granulometry Indicator Based on Digital Image Processing”, IJEEI, vol. 14, no. 1, pp. 118–134, Mar. 2026, doi: 10.52549/ijeei.v14i1.7180.