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.
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References
Winegardner, D.L.: An Introduction to Soils for Environmental Professionals. Lewis Publishers (1995)
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)
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
Kim, Y., Yun, T.S.: How to classify sand types: a deep learning approach. Eng. Geol. 288(1), 106142 (2021)
Kim, Y., et al.: Determination of shape parameters of sands: a deep learning approach. Acta Geotech. 17, 1521–1531 (2022)
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
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
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)
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)
Maitre, J., Bouchard, K., Bedard, L.P.: Mineral grains recognition using computer vision and machine learning. Comput. Geosci. 130, 84–93 (2019)
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)
Wu, X., et al.: Digital holographic sizer for coal powder size distribution measurement: preliminary simulation and experiment. Meas. Sci. Technol. 29(12), 124001 (2018)
Yang, X., Ren, T., Tan, L.: Size distribution measurement of coal fragments using digital imaging processing. Measurement 160, 107867 (2020)
Hr, A., et al.: Automated segmentation of gravel particles from depth images of gravel-soil mixtures - ScienceDirect. Comput. Geosci. 128, 1–10 (2019)
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)
Engin, I.C., Maerz, N.H.: Size distribution analysis of aggregates using LiDAR scan data and an alternate algorithm. Measurement 143, 136 (2019)
Zheng, W., et al.: Characterization of two- and three-dimensional morphological properties of fragmented sand grains. Eng. Geol. 263, 105358 (2019)
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)
Li, J., Shao, S., Hong, J.: Machine learning shadowgraph for particle size and shape characterization. Meas. Sci. Technol. 32(1), 015406 (2021)
Padarian, J., Minasny, B., Mcbratney, A.B.: Using deep learning to predict soil properties from regional spectral data. Geoderma Region. 16, e00198 (2018)
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)
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)
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)
Redmon, J., Farhadi A.: YOLOv3: An Incremental Improvement. arXiv e-prints (2018)
He, K., Gkioxari, G., Dollár, P., et al.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386–397 (2020)
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)
Krizhevsky, A., Sutskever I., Hinton G.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25(2) (2012)
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
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(6), 679–698 (1986)
Vincent, L.U.: Morphological gray scale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Process. 2(2), 176–201 (1993)
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Thanks to the great motherland.
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This work was partially supported by the University Natural Science Research Project of Anhui Province [China] (KJ2020A0238, KJ2019A0049).
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XL: Methodology, Software, Writing. ZY: Formal analysis, Validation. XT: Data Curation. XW: Investigation, Visualization. YH: Resources. XM: Conceptualization. XH: Supervision.
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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
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DOI: https://doi.org/10.1007/s11760-023-02696-8