Skip to main content
SpringerLink
Log in

Stone segmentation based on improved U-Net network

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

Abstract

In the contemporary stone aggregate industry and regulatory environment, there is a pressing need for a method that can quickly and efficiently calculate the average particle size of a batch of stones. The traditional manual sampling and measurement approach faces numerous challenges, such as the inability to accurately reflect the true average particle size of the stones, the risk of rough stones damaging measuring equipment, high labor costs, and potential inaccuracies in measurement data due to environmental factors like quarries. This study introduces computer vision technology, using image segmentation techniques to calculate stone particle sizes. The article innovates on the U-Net network, demonstrating through comparative experiments that these improvements significantly enhance the effectiveness of image segmentation. Accurate image segmentation is crucial for subsequent particle size calculations, not only advancing the development of the stone aggregate field but also promoting the intelligent evolution of the financial and tax supervision industry.

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

Data availability

The dataset has been rigorously tested and filtered, and it will soon be made available for open access at https://github.com/Mxk-1.

References

  1. Wang, Y., Ye, G., Zhang, Y., Ping, M., Wang, H.: Is the Chinese construction industry moving towards a knowledge-and technology-intensive industry? J. Clean. Prod. 259, 120964 (2020)

    Article  Google Scholar 

  2. Kim, H., Haas, C.T., Rauch, A.F., Browne, C.: Dimensional ratios for stone aggregates from three-dimensional laser scans. J. Comput. Civil Eng. 16(3), 175–183 (2002)

    Article  Google Scholar 

  3. Stone, K.J.L., Wood, D.M.: Effects of dilatancy and particle size observed in model tests on sand. Soils Found. 32(4), 43–57 (1992)

    Article  Google Scholar 

  4. Alm, J.: What is an’’ optimal’’ tax system? National Tax J. 49(1), 117–133 (1996)

    Article  Google Scholar 

  5. Merkus, H.G.: Particle Size Measurements: Fundamentals, Practice, Quality, vol. 17. Springer Science & Business Media, Berlin (2009)

    Google Scholar 

  6. Galetakis, M., Soultana, A.: A review on the utilisation of quarry and ornamental stone industry fine by-products in the construction sector. Constr. Build. Mater. 102, 769–781 (2016)

    Article  Google Scholar 

  7. Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(7), 3523–3542 (2021)

    Google Scholar 

  8. Rankohi, S., Waugh, L.: Review and analysis of augmented reality literature for construction industry. Visualization in Engineering 1(1), 1–18 (2013)

    Article  Google Scholar 

  9. Chen, J.: Design and implementation of sand and gravel particle size detection system. Master’s thesis, Harbin Institute of Technology, (2022)

  10. Geng, G.Y.F., Li, L.P.W., Bo, Y.: Quantification method of sand and gravel powder segmentation based on improved unet network. Comput. Syst. Appl. 31, 213–221 (2022)

  11. Luo, C.: Research on real-time detection system for sand and gravel gradation based on machine vision. Master’s Thesis, Wuhan University of Technology, (2023)

  12. Gao, L.W.M., Shi, W.C.Y., Hu, M.F.: Research on sand and gravel segmentation algorithm based on improved mask r-cnn algorithm. J. Wuhan Univ. Technol. 42, 56–63 (2020)

  13. Zhu, D.: Particle size detection of sand and gravel images based on deep learning. Master’s Thesis, NanJing University of Technology, (2022)

  14. Bhargavi, K., Jyothi, S.: A survey on threshold based segmentation technique in image processing. Int. J. Innov. Res. Develop. 3(12), 234–239 (2014)

    Google Scholar 

  15. Kaganami, H.G., Beiji, Z.: Region-based segmentation versus edge detection. In: 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1217–1221. IEEE, (2009)

  16. Lyu, H., Fu, H., Hu, X., Liu., L.: Esnet: Edge-based segmentation network for real-time semantic segmentation in traffic scenes. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 1855–1859. IEEE, (2019)

  17. Aly, A.A., Deris, S.B., Zaki, N.: Research review for digital image segmentation techniques. Int. J. Comput. Sci. Inform. Technol. 3(5), 99 (2011)

    Google Scholar 

  18. McCann, D.M., Jackson, P.D., Fenning, P.J.: Comparison of the seismic and ground probing radar methods in geological surveying. In: IEE Proceedings F (Communications, Radar and Signal Processing), pp. 380–391. IET, (1988)

  19. Singh, B., Najibi, M., Davis, L.S.: Sniper: Efficient multi-scale training. Advances in Neural Information Processing Systems, 31, (2018)

  20. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, (2015)

  21. Kopf, J., Cohen, M.F., Lischinski, D., Uyttendaele, M.: Joint bilateral upsampling. ACM Trans. Graph. 26(3), 96 (2007)

    Article  Google Scholar 

  22. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, (2014)

  23. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, (2016)

  24. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9, (2015)

  25. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800–1807, (2016)

  26. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861, (2017)

  27. Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 472–480, (2017)

  28. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional Block Attention Module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3–19, (2018)

  29. Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, (2016)

  30. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814, (2010)

  31. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778, (2016)

  32. Sheng, X., Kang, C., Zheng, J., Lyu, C.: An edge-guided method to fruit segmentation in complex environments. Comput. Electron. Agric. 208, 107788 (2023)

    Article  Google Scholar 

  33. Hobby, J.D.: Practical segment intersection with finite precision output. Comput. Geom. 13(4), 199–214 (1999)

    Article  MathSciNet  Google Scholar 

  34. Buckland, M., Gey, F.: The relationship between recall and precision. Journal of the American society for information science 45(1), 12–19 (1994)

    Article  Google Scholar 

Download references

Funding

This research is partially supported by the Natural Science Project of Chongqing University of Science and Technology (Grant No. 20220211) and the collaborative project between Chongqing University and the Chinese Academy of Sciences on ‘Key Technologies and Collaborative Innovation of Industrial Internet Endogenous Security’ (Grant No. HZ20211015).

Author information

Authors and Affiliations

  1. Chongqing University of Science and Technology, Chongqing, 401331, China

    Ning Chen, Xinkai Ma, Haixia Luo, Jun Peng, Shangzhu Jin, Xiao Wu & Yongsheng Zhou

Authors
  1. Ning Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinkai Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haixia Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shangzhu Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yongsheng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.C. contributed to conceptualization and methodology, X.M. was involved in formal analysis, H.L. contributed to visualization and investigation, J.P. was involved in review and editing, S.J. contributed to supervision, X.W. was involved in validation, and Y.Z. contributed to funding acquisition.

Corresponding author

Correspondence to Ning Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Not 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

Chen, N., Ma, X., Luo, H. et al. Stone segmentation based on improved U-Net network. SIViP (2024). https://doi.org/10.1007/s11760-024-03201-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-024-03201-5

Keywords

Search

Navigation