Skip to main content
SpringerLink
Log in

Particle recognition and shape parameter detection based on deep learning

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

The size and shape parameters of sand particles are closely related to their geophysical and geomechanical properties. It is challenging to accurately identify sand particles and calculate their shape parameters. In this study, a convolutional neural network was used to detect sand particles in sample images and further calculate their size parameters. Using Mask R-CNN as the benchmark detection network, by analyzing the labeling data of sand particles, comparing the size of different a priori boxes to obtain better detection results. In addition, this study uses an edge detection algorithm with adaptive parameters to segment the particle region of interest, and combines the mask predicted by the network model to select the segmented region belonging to the object. The image processing algorithm can be used to segment the area more accurately, and the deep learning algorithm can detect the target more robustly, and combine the two to calculate the parameters of the particles.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig.2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

  1. Winegardner, D.L.: An Introduction to Soils for Environmental Professionals. Lewis Publishers (1995)

    Google Scholar 

  2. Suh, H.S., et al.: Quantification of bulk form and angularity of particle with correlation of shear strength and packing density in sands. Eng. Geol. 220, 256–265 (2017)

    Article  Google Scholar 

  3. Altuhafi, F.N., Coop, M.R., Georgiannou, V.N.: Effect of particle shape on the mechanical behavior of natural sands. J. Geotech. Geoenviron. Eng. 142(12), 04016071 (2016). https://doi.org/10.1061/(ASCE)GT.1943-5606.0001569

    Article  Google Scholar 

  4. Kim, Y., Yun, T.S.: How to classify sand types: a deep learning approach. Eng. Geol. 288(1), 106142 (2021)

    Article  Google Scholar 

  5. Kim, Y., et al.: Determination of shape parameters of sands: a deep learning approach. Acta Geotech. 17, 1521–1531 (2022)

    Article  MathSciNet  Google Scholar 

  6. Yang, J., Luo, X.D.: Exploring the relationship between critical state and particle shape for granular materials. J. Mech. Phys. Solids 84, 196–213 (2015). https://doi.org/10.1016/j.jmps.2015.08.001

    Article  Google Scholar 

  7. Kumar, A., Ghosh, S.K.: Size distribution analysis of wear debris generated in HEMM engine oil for reliability assessment: a statistical approach. Measurement 131, 412–418 (2019). https://doi.org/10.1016/j.measurement.2018.09.012

    Article  Google Scholar 

  8. Sun, Q., Zheng, J., Li, C.: Improved watershed analysis for segmenting contacting particles of coarse granular soils in volumetric images. Powder Technol. 356, 295–303 (2019)

    Article  Google Scholar 

  9. Banerjee, S., Chakraborti, P.C., Saha, S.K.: An automated methodology for grain segmentation and grain size measurement from optical micrographs. Measurement 140, 142–150 (2019)

    Article  Google Scholar 

  10. Maitre, J., Bouchard, K., Bedard, L.P.: Mineral grains recognition using computer vision and machine learning. Comput. Geosci. 130, 84–93 (2019)

    Article  Google Scholar 

  11. Yang, J.H., Fang, H.Y., Chen, S.J.: Development of particle size and shape measuring system for machine-made sand. Part. Sci. Technol. 37(8), 974–980 (2018)

    Article  Google Scholar 

  12. Wu, X., et al.: Digital holographic sizer for coal powder size distribution measurement: preliminary simulation and experiment. Meas. Sci. Technol. 29(12), 124001 (2018)

    Article  Google Scholar 

  13. Yang, X., Ren, T., Tan, L.: Size distribution measurement of coal fragments using digital imaging processing. Measurement 160, 107867 (2020)

    Article  Google Scholar 

  14. Hr, A., et al.: Automated segmentation of gravel particles from depth images of gravel-soil mixtures - ScienceDirect. Comput. Geosci. 128, 1–10 (2019)

    Article  Google Scholar 

  15. Kling, N.H., et al.: Dual-plane stereo-astigmatism—A novel method to determine the full velocity gradient tensor in planar domain. Meas. Sci. Technol. 31(8), 085202 (2020)

    Article  Google Scholar 

  16. Engin, I.C., Maerz, N.H.: Size distribution analysis of aggregates using LiDAR scan data and an alternate algorithm. Measurement 143, 136 (2019)

    Article  Google Scholar 

  17. Zheng, W., et al.: Characterization of two- and three-dimensional morphological properties of fragmented sand grains. Eng. Geol. 263, 105358 (2019)

    Article  Google Scholar 

  18. Liang, Z., Nie, Z., An, A., et al.: A particle shape extraction and evaluation method using a deep convolutional neural network and digital image processing. Powder Technol. 353, 156–170 (2019)

    Article  Google Scholar 

  19. Li, J., Shao, S., Hong, J.: Machine learning shadowgraph for particle size and shape characterization. Meas. Sci. Technol. 32(1), 015406 (2021)

    Article  Google Scholar 

  20. Padarian, J., Minasny, B., Mcbratney, A.B.: Using deep learning to predict soil properties from regional spectral data. Geoderma Region. 16, e00198 (2018)

    Article  Google Scholar 

  21. Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)

    Article  Google Scholar 

  22. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol. 9351, pp. 234–241. Springer Verlag (2015)

  23. Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  24. Redmon, J., Farhadi A.: YOLOv3: An Incremental Improvement. arXiv e-prints (2018)

  25. He, K., Gkioxari, G., Dollár, P., et al.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386–397 (2020)

    Article  Google Scholar 

  26. Chen, H., et al.: BlendMask: top-down meets bottom-up for instance segmentation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) IEEE (2020)

  27. Krizhevsky, A., Sutskever I., Hinton G.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25(2) (2012)

  28. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Piotr Dollár, C., Zitnick, L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, Bernt, Tuytelaars, T. (eds.) Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V, pp. 740–755. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  29. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(6), 679–698 (1986)

    Article  Google Scholar 

  30. Vincent, L.U.: Morphological gray scale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2(2), 176–201 (1993)

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to the great motherland.

Funding

This work was partially supported by the University Natural Science Research Project of Anhui Province [China] (KJ2020A0238, KJ2019A0049).

Author information

Authors and Affiliations

  1. School of Mathematics and Physics, Anhui University of Technology, Maanshan, 243002, China

    Xuan Li, Zhou Yang, Xinyu Tao, Xiaojie Wang, Yufeng Han, Xutao Mo & Xianshan Huang

Authors
  1. Xuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhou Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xinyu Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaojie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yufeng Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xutao Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xianshan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XL: Methodology, Software, Writing. ZY: Formal analysis, Validation. XT: Data Curation. XW: Investigation, Visualization. YH: Resources. XM: Conceptualization. XH: Supervision.

Corresponding authors

Correspondence to Xutao Mo or Xianshan Huang.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval and consent to participate

Not applicable.

Consent for publication

Applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Yang, Z., Tao, X. et al. Particle recognition and shape parameter detection based on deep learning. SIViP 18, 81–89 (2024). https://doi.org/10.1007/s11760-023-02696-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02696-8

Keywords

Search

Navigation