Estimating volume of oversized copper ore chunks in an underground mine using a laser scanner and an RGB camera for hammering efficiency assessment

References

• S. Yan, and J. Xu, Study on dynamic characteristics of a hydraulic hammer, 2010 International Conference on Digital Manufacturing & Automation, Changcha, Hunan China, Vol. 2, IEEE, 2010, pp. 459–462.
   Google Scholar
• J. Pacheco, and J. de Brito, Recycled aggregates produced from construction and demolition waste for structural concrete: Constituents, properties and production, Materials. 14 (19) (2021), pp. 5748. doi:10.3390/ma14195748.
   PubMed Web of Science ®Google Scholar
• A.B. Gogolewska, and M.M. Kowalczyk, Group winning blasting as a measure to mitigate seismic hazard in a deep copper ore mine, SW poland, Min. Scince. 27 (2020). doi:10.37190/msc202711.
   Google Scholar
• T. Kujundžić, G. Bedeković, D. Kuhinek, and T. Korman, Impact of rock hardness on fragmentation by hydraulic hammer and crushing in jaw crusher, Rudarskogeološko-naftni zbornik. 20 (2008), pp. 83–90.
   Google Scholar
• E.C. Poeck, Performance evaluation of various nozzle designs for waterjet scaling in underground excavations, a, 2000-2009-mines theses & dissertations, Int. J. Eng. Technol. Manage. Res. 10 (25) (2008). doi:10.29121/ijetmr.v9.i10.2022.1232.
   Google Scholar
• P. Stefaniak, J. Wodecki, J. Jakubiak, and R. Zimroz, Development of test rig for robotization of mining technological processes–oversized rock breaking process case, IOP Conference Series: Earth and Environmental Science, Prague, Czech Republic, Vol. 95, IOP Publishing, 2017, p. 042028.
   Google Scholar
• Z. Xu, and G. Yang, Modeling and simulation of hydraulic hammer for sleeve valve, Engineering. 8 (9) (2016), pp. 657–668. doi:10.4236/eng.2016.89059.
   Google Scholar
• A. Wróblewski, P. Krot, R. Zimroz, T. Mayer, and J. Peltola, Review of linear electric motor hammers—an energy-saving and eco-friendly solution in industry, Energies. 16 (2) (2023), pp. 959. doi:10.3390/en16020959.
   Web of Science ®Google Scholar
• N. Decker, Y. Wang, and Q. Huang, Efficiently registering scan point clouds of 3D printed parts for shape accuracy assessment and modeling, J. Manuf. Syst. 56 (2020), pp. 587–597. doi:10.1016/j.jmsy.2020.04.001.
   Web of Science ®Google Scholar
• M.A. Carreira-Perpinán, A Review of Mean-Shift Algorithms for Clustering, arXiv preprint arXiv. 1503.00687. 2015.
   Google Scholar
• D. Comaniciu, V. Ramesh, and P. Meer, Kernel-based object tracking, IEEE T. Pattern Anal. 25 (5) (2003), pp. 564–577. doi:10.1109/TPAMI.2003.1195991.
   Web of Science ®Google Scholar
• W. Tao, H. Jin, and Y. Zhang, Color image segmentation based on mean shift and normalized cuts, IEEE Transactions On Systems, Man, And Cybernetics, Part B (Cybernetics). 37 (5) (2007), pp. 1382–1389. doi:10.1109/TSMCB.2007.902249.
   PubMed Web of Science ®Google Scholar
• T.T. Nguyen, X.G. Liu, Y. Huang, H.P. Wang, Q.L. Kieu, and T.S. Dang, Ore volume measurement based on 3d laser scanning technique: a case study, Adv. Mater. Res. Trans. Tech. Publ. 610-613 net/amr.610-613.3708., (2012), pp. 3708–3714. doi:10.4028/www.scientific.
   Google Scholar
• A. Septarini, B. Soeksmantono, and K. Wikantika, Lidar application study to calculate mine excavation, 34th Asian Conference on Remote Sensing, Bali, Indonesia, Vol. 1, Asian Association on Remote Sensing, 2013.
   Google Scholar
• B. Jawecki, P.B. Da¸bek, K. Pawęska, and X. Wei, Estimating water retention in post-mining excavations using LiDAR ALS data for the strzelin quarry, in lower Silesia, Mine Water. Environ. 37, 4 (2018), pp. 744–753. doi:10.1007/s10230-018-0526-0.
   Web of Science ®Google Scholar
• Y. Wang, M. Ding, Q. Zhang, X. Zhang, and Z. Qu, Volume calculation methods of irregular stone artifacts based on 3d laser scanning technology, J. Asian Archit. Build. (2023), pp. 1–17. doi:10.1080/13467581.2023.2182640.
   Web of Science ®Google Scholar
• A. Bienert, C. Hess, H.-G. Maas, and G. von Oheimb, A voxel-based technique to estimate the volume of trees from terrestrial laser scanner data, the International archives of the photogrammetry, Remote sensing and spatial information sciences XL-5, (2014), pp. 101–106. doi: 10.5194/isprsarchives-XL-5-101-2014.
   Google Scholar
• CloudCompareV2.13 gpl software, https://www.cloudcompare.org/, accessed: 2023-05-23.
   Google Scholar
• J.-D. Boissonnat, Geometric structures for three-dimensional shape representation, ACM Trans. Graphics. 3 (4) (1984), pp. 266–286. doi:10.1145/357346.357349.
   Web of Science ®Google Scholar
• R.W. Lewis, Y. Zheng, and D.T. Gethin, Three-dimensional unstructured mesh generation: Part 3. volume meshes, Comput. Methods Appl. Mech. Eng. 134 (3–4) (1996), pp. 285–310. doi:10.1016/0045-7825(95)00918-3.
   Web of Science ®Google Scholar
• D. Bonneau, P.-M. DiFrancesco, and D.J. Hutchinson, Surface Reconstruction for three-dimensional Rockfall volumetric analysis, ISPRS Int. J. Geo-Inf. 8 (2019 12), pp. 548. doi:10.3390/ijgi8120548.
   Web of Science ®Google Scholar
• V. MeshLab. 2022.02, https://www.meshlab.net/, accessed: 2023-05-23.
   Google Scholar
• W.-C. Chang, C.-H. Wu, Y.-H. Tsai, and W.-Y. Chiu, Object volume estimation based on 3d point cloud, 2017 International Automatic Control Conference (CACS), 2017, pp. 1–5. doi:10.1109/CACS.2017.8284244.
   Google Scholar
• B. Li, J. Wei, L. Wang, B. Ma, and M. Xu, A comparative analysis of two point cloud volume calculation methods, Int. J. Remote Sens. 40 (8) (2019), pp. 3227–3246. doi:10.1080/01431161.2018.1541111.
   Web of Science ®Google Scholar
• E.M. Mikhail, J.S. Bethel, and J.C. McGlone, Introduction to Modern Photogrammetry, Hoboken, New Jersey: John Wiley & Sons, 2001.
   Google Scholar
• T. Luhmann, S. Robson, S. Kyle, and I. Harley, Close Range Photogrammetry: Principles, Techniques and Applications, Vol. 3, Dunbeath, Great Britain: Whittles publishing Dunbeath, 2006.
   Google Scholar
• F. Remondino, A. Guarnieri, and A. Vettore, 3d modeling of close-range objects: Photogrammetry or laser scanning? Videometrics VIII, Bertinoro, Italy, Vol. 5665, SPIE, 2005, pp. 216–225.
   Google Scholar
• A. Buyer, S. Aichinger, and W. Schubert, Applying photogrammetry and semi-automated joint mapping for rock mass characterization, Eng. Geol. 264 (2020), pp. 105332. doi:10.1016/j.enggeo.2019.105332.
   Web of Science ®Google Scholar
• D. Kong, C. Saroglou, F. Wu, P. Sha, and B. Li, Development and application of uav-sfm photogrammetry for quantitative characterization of rock mass discontinuities, Int. J. Rock Mech. Min. Sci. 141 (2021), pp. 104729. doi:10.1016/j.ijrmms.2021.104729.
   Web of Science ®Google Scholar
• M.J. Herrero, A.P. Pérez-Fortes, J.I. Escavy, J.M. Insua-Arévalo, R. De la Horra, F. López-Acevedo, and L. Trigos, 3d model generated from uav photogrammetry and semi-automated rock mass characterization, Comput Geosci 163(2022), pp. 105121. doi:10.1016/j.cageo.2022.105121.
   Web of Science ®Google Scholar
• D.J. Benton, S.R. Iverson, L.A. Martin, J.C. Johnson, and M.J. Raffaldi, Volumetric measurement of rock movement using photogrammetry, Int. J. Min. Sci. Technol 26 (1) (2016), pp. 123–130. doi:10.1016/j.ijmst.2015.11.020.
   PubMedGoogle Scholar
• B. Slaker, Monitoring underground mine displacement using photogrammetry and laser scanning, Ph.D. thesis, Virginia Tech (2015).
   Google Scholar
• H. Toriya, Z.P.L. Tungol, H. Ikeda, N. Owada, H.D. Jang, T. Adachi, I. Kitahara, and Y. Kawamura, Fragmentation size distribution measurement by GNSS-aided photogrammetry at real mine site, mining, Mining. 2 (3) (2022), pp. 438–448. doi:10.3390/mining2030023.
   Google Scholar
• P.J. Bardziński, B. Doroszuk, W. Kawalec, R. Król, Investigation of grain size distribution of conveyed copper ore for modelling ore flow through a bunker, IOP Conf. Ser.: Earth Environ. Sci. 609, 1 (2020), pp. 012105. doi:10.1088/1755-1315/609/1/012105.
   Google Scholar
• H. Jang, I. Kitahara, Y. Kawamura, Y. Endo, E. Topal, R. Degawa, S. Mazara, Development of 3d rock fragmentation measurement system using photogrammetry, Int. J. Min. Reclam. Environ. 34, 4 (2019), pp. 294–305. doi:10.1080/17480930.2019.1585597.
   Web of Science ®Google Scholar
• R. Goyal, A survey of diverse segmentation methods in image processing, 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET), Bhopal, Madhya Pradesh, IEEE, 2022, pp. 1–5.
   Google Scholar
• W. Jasim and R. Mohammed, A survey on segmentation techniques for image processing, Iraqi J. Electr. Electron. Eng. 17 (2) (2021), pp. 73–93. doi:10.37917/ijeee.17.2.10.
   Google Scholar
• S. Lampinen, and J. Mattila, Robust rock detection and clustering with surface analysis for robotic rock breaking systems, 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2021, pp. 140–147. doi:10.1109/AIM46487.2021.9517695.
   Google Scholar
• M. Siami, T. Barszcz, J. Wodecki, and R. Zimroz, Design of an infrared image processing pipeline for robotic inspection of conveyor systems in opencast mining sites, Energies. 15 (18) (2022), pp. 6771. doi:10.3390/en15186771.
   Web of Science ®Google Scholar
• P. Da¸bek, J. Szrek, R. Zimroz, and J. Wodecki, An automatic procedure for overheated idler detection in belt conveyors using fusion of infrared and rgb images acquired during ugv robot inspection, Energies. 15 (2) (2022), pp. 601. doi:10.3390/en15020601.
   Web of Science ®Google Scholar
• M. Huang, Y. Liu, and Y. Yang, Edge detection of ore and rock on the surface of explosion pile based on improved canny operator, Alex. Eng. J. 61 (12) (2022), pp. 10769–10777. doi:10.1016/j.aej.2022.04.019.
   Web of Science ®Google Scholar
• P. Da¸bek, A. Wróblewski, J. Wodecki, P. Bortnowski, M. Ozdoba, R. Król, and R. Zimroz, Application of the methods of monitoring and detecting the belt mistracking in laboratory conditions, Appl. Sci. 13 (2023 4), pp. 2111. doi:10.3390/app13042111.
   Google Scholar
• X. Ren, and J. Malik, Learning a classification model for segmentation, Ninth IEEE International Conference on Computer Vision, Nice, France, Vol. 2, IEEE Computer Society, 2003, pp. 10–10.
   Google Scholar
• Z. Sun, P. Xuan, Z. Song, H. Li, and R. Jia, A texture fused superpixel algorithm for coal mine waste rock image segmentation, Int. J. Coal Prep. Util. 42 (4) (2022), pp. 1222–1233. doi:10.1080/19392699.2019.1699546.
   Google Scholar
• P. Loncomilla, P. Samtani, and J.R. Del Solar, Detecting rocks in challenging mining environments using convolutional neural networks and ellipses as an alternative to bounding boxes, Expert Syst Appl. 194 (2022), pp. 116537. doi:10.1016/j.eswa.2022.116537.
   Web of Science ®Google Scholar
• P. Loncomilla, P. Samtani, and J.R. Del Solar, Rocky-centernet: Detecting rocks using convolutional neural networks and ellipses, Software Imp. 12 (2022), pp. 100290. doi:10.1016/j.simpa.2022.100290.
   Google Scholar
• D. Cárdenas, I. Parra-Tsunekawa, F. Leiva, and J. Ruiz-Del Solar, Automatic determination of rock-breaking target poses for impact hammers, Energies. 15 (17) (2022), pp. 6380. doi:10.3390/en15176380.
   Web of Science ®Google Scholar
• P. Bortnowski, H. Gondek, R. Król, D. Marasova, and M. Ozdoba, Detection of blockages of the belt conveyor transfer point using an RGB camera and CNN autoencoder, Energies. 16 (4) (2023), pp. 1666. doi:10.3390/en16041666.
   Web of Science ®Google Scholar
• M. Siami, T. Barszcz, J. Wodecki, and R. Zimroz, Automated identification of overheated belt conveyor idlers in thermal images with complex backgrounds using binary classification with CNN, Sensors. 22 (24) (2022), pp. 10004. doi:10.3390/s222410004.
   PubMed Web of Science ®Google Scholar
• Livox avia quick start guide, Brochure (2021). URL https://terra-1-g.djicdn.com/65c028cd298f4669a7f0e40e50ba1131/Download/Avia/Livox%20Avia_Quick%20Start%20Guide_V1.4.pdf
   Google Scholar
• Livox horizon user manual, Brochure (2019). URLhttps://www.livoxtech.com/3296f540ecf5458a8829e01cf429798e/assets/horizon/Livox%20Hozon%20user%20manual%20v1.0.pdf
   Google Scholar
• A. Carrilho, M. Galo, and R.C. Dos Santos, Statistical outlier detection method for airborne LIDAR data., International archives of the photogrammetry, Remote Sens. Spatial Inf. Sci. 42 (1) (2018), pp. 87–92. doi:10.5194/isprs-archives-XLII-1-87-2018.
   Google Scholar
• CloudCompare: Align, https://www.cloudcompare.org/doc/wiki/index.php? title=Align, accessed: 2023-05-14.
   Google Scholar
• CloudCompare: Rasterize, https://www.cloudcompare.org/doc/wiki/index. php/Rasterize#Interpolating_empty_cells, accessed: 2023-05-14.
   Google Scholar
• CloudCompare: Compute 2.5D volume, https://www.cloudcompare.org/doc/wiki/index.php?title=2.5D_Volume, accessed: 2023-05-14.
   Google Scholar