CNN-Based Automatic Detection of Beachlines Using UAVs for Enhanced Waste Management in Tailings Storage Facilities
Source abstract
Abstract
Continuous monitoring is key to the safety of such critical infrastructure as Tailings storage facilities. Due to the high risk of liquification of the dams, it is crucial to move the water as far as possible from the dam crest. In order to control the distance from the water to the dam, regular manual inspections need to be carried out. In this article, we propose a method for automatic detection of the water-beach line based on photographs from an unmanned aerial vehicle (UAV). An algorithm based on MobileNet v2 convolutional neural network architecture was developed for the classification of images collected by the UAV. Based on the results of this classification, the border between the water and the beach is defined. Several approaches to the model training were tested. Accuracy for the validation set reaches up to 97% for particular image fragments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Geophysical Methods and Applications · Tailings Management and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
KGHM CUPRUM Ltd.—Research and Development Centre, Gen. W. Sikorskiego Street 2-8, 53-659 Wrocław, Poland
KGHM Polska Miedź S.A., ul. M. Skłodowskiej-Curie 48, 59-301 Lubin, Poland
Funding
EIT RawMaterials GmbH under Framework Partnership Agreement
References (52)
Show source-provided references
1. Critical review of tailings dam monitoring best practice10.1080/17480930.2019.1625172 ↗
2. The internet of things (IOT) and cloud computing (CC) based tailings dam monitoring and pre-alarm system in mines10.1016/j.ssci.2011.08.028 ↗
3. Pre-alarm system based on real-time monitoring and numerical simulation using internet of things and cloud computing for tailings dam in mines10.1109/ACCESS.2017.2753379 ↗
4. The Tailings Storage Facility (TSF) stability monitoring system using advanced big data analytics on the example of the Żelazny Most Facility
5. Duda, N., Jachnik, B., Stefaniak, P., Bursa, B., and Stefanek, P. (September, January 30). Tailings storage facility stability monitoring using CPT data analytics on the Zelazny Most facility. Proceedings of the Application of Computers and Operations Research in the Mineral Industries” (APCOM 2021): Minerals Industry 4.0: The Next Digital Transformation in Mining, Johannesburg, South Africa.
6. Koperska, W., Stachowiak, M., Jachnik, B., Stefaniak, P., Bursa, B., and Stefanek, P. (2021). Machine Learning Methods in the Inclinometers Readings Anomaly Detection Issue on the Example of Tailings Storage Facility. IFIP International Workshop on Artificial Intelligence for Knowledge Management, Springer.10.1007/978-3-030-80847-1_15 ↗
7. Duda-Mróz, N., Koperska, W., Stefaniak, P., Anufriiev, S., Stachowiak, M., and Stefanek, P. (2023). Application of Dynamic Time Warping to Determine the Shear Wave Velocity from the Down-Hole Test. Appl. Sci., 13.10.3390/app13179736 ↗
8. Shoreline definition and detection: A review10.2112/03-0071.1 ↗
9. Toure, S., Diop, O., Kpalma, K., and Maiga, A.S. (2019). Shoreline detection using optical remote sensing: A review. ISPRS Int. J. Geo-Inf., 8.10.3390/ijgi8020075 ↗
10. Abdelhady, H.U., Troy, C.D., Habib, A., and Manish, R. (2022). A simple, fully automated shoreline detection algorithm for high-resolution multi-spectral imagery. Remote Sens., 14.10.3390/rs14030557 ↗
11. Automatic shoreline detection and change detection analysis of netravati-gurpurrivermouth using histogram equalization and adaptive thresholding techniques10.1016/j.aqpro.2015.02.073 ↗
12. Tajima, Y., Wu, L., and Watanabe, K. (2021). Development of a shoreline detection method using an artificial neural network based on satellite SAR imagery. Remote Sens., 13.10.3390/rs13122254 ↗
13. The performance of shoreline detection models applied to video imagery10.2112/1551-5036(2007)23[658:TPOSDM]2.0.CO;2 ↗
14. Applications of UAVs in mine industry: A scoping review10.46873/2300-3960.1384 ↗
15. A review of UAV monitoring in mining areas: Current status and future perspectives10.1007/s40789-019-00264-5 ↗
16. Unmanned aerial vehicles in agriculture: A review of perspective of platform, control, and applications10.1109/ACCESS.2019.2932119 ↗
17. Shahmoradi, J., Talebi, E., Roghanchi, P., and Hassanalian, M. (2020). A comprehensive review of applications of drone technology in the mining industry. Drones, 4.10.3390/drones4030034 ↗
18. Park, S., and Choi, Y. (2020). Applications of unmanned aerial vehicles in mining from exploration to reclamation: A review. Minerals, 10.10.3390/min10080663 ↗
19. Use of low-cost UAV photogrammetry to analyze the accuracy of a digital elevation model in a case study10.1016/j.measurement.2016.05.028 ↗
20. Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges10.1016/j.tra.2020.09.018 ↗
21. Kanellakis, C., and Nikolakopoulos, G. (2016, January 21–24). Evaluation of visual localization systems in underground mining. Proceedings of the 2016 24th Mediterranean Conference on Control and Automation (MED), Athens, Greece.10.1109/MED.2016.7535853 ↗
22. Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges10.1109/ACCESS.2019.2909530 ↗
23. Unmanned aerial vehicle classification, applications and challenges: A review
24. Topographic modelling using UAVs compared with traditional survey methods in mining10.1590/0370-44672017710074 ↗
25. Stead, D., Donati, D., Wolter, A., and Sturzenegger, M. (2019). Application of remote sensing to the investigation of rock slopes: Experience gained and lessons learned. ISPRS Int. J. Geo-Inf., 8.10.3390/ijgi8070296 ↗
26. Filipova, S., Filipov, D., and Raeva, P. (2016, January 13–17). Creating 3D model of an open pit quarry by UAV imaging and analysis in GIS. Proceedings of the 6th International Conference on Cartography & GIS, Albena, Bulgaria.
27. Open-pit mine geomorphic changes analysis using multi-temporal UAV survey10.1007/s12665-018-7383-9 ↗
28. UAV photogrammetry-based for open pit coal mine large scale mapping, case studies in Cam Pha City, Vietnam
29. Bui, D.T., Long, N.Q., Bui, X.-N., Nguyen, V.-N., Van Pham, C., Van Le, C., Ngo, P.-T.T., Bui, D.T., and Kristoffersen, B. Lightweight unmanned aerial vehicle and structure-from-motion photogrammetry for generating digital surface model for open-pit coal mine area and its accuracy assessment. Advances and Applications in Geospatial Technology and Earth Resources: Proceedings of the International Conference on Geo-Spatial Technologies and Earth Resources, Springer.
30. Building DEM for deep open-pit coal mines using DJI Inspire 2
31. Jakob, S., Zimmermann, R., and Gloaguen, R. (2016, January 21–24). Processing of drone-borne hyperspectral data for geological applications. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.10.1109/WHISPERS.2016.8071689 ↗
32. Automated lithological classification using UAV and machine learning on an open cast mine10.1080/25726838.2019.1578031 ↗
33. Drones in carbonate geology: Opportunities and challenges, and application in diagenetic dolomite geobody mapping10.1016/j.marpetgeo.2018.02.002 ↗
34. Kirsch, M., Lorenz, S., Zimmermann, R., Tusa, L., Möckel, R., Hödl, P., Booysen, R., Khodadadzadeh, M., and Gloaguen, R. (2018). Integration of terrestrial and drone-borne hyperspectral and photogrammetric sensing methods for exploration mapping and mining monitoring. Remote Sens., 10.10.3390/rs10091366 ↗
35. Jakob, S., Zimmermann, R., and Gloaguen, R. (2017). The need for accurate geometric and radiometric corrections of drone-borne hyperspectral data for mineral exploration: Mephysto—A toolbox for pre-processing drone-borne hyperspectral data. Remote Sens., 9.10.3390/rs9010088 ↗
36. Applications of point cloud technology in geomechanical characterization, analysis and predictive modeling
37. Raj, P. (2019). Use of Drones in an Underground Mine for Geotechnical Monitoring. [Master’s Thesis, The University of Arizona].
38. Turner, R.M., Bhagwat, N.P., Galayda, L.J., Knoll, C.S., Russell, E.A., and MacLaughlin, M.M. (2018, January 17–20). Geotechnical characterization of underground mine excavations from UAV-captured photogrammetric & thermal imagery. Proceedings of the 52nd ARMA US Rock Mechanics/Geomechanics Symposium, Seattle, WA, USA.
39. Lyu, M., Zhao, Y., Huang, C., and Huang, H. (2023). Unmanned aerial vehicles for search and rescue: A survey. Remote Sens., 15.10.3390/rs15133266 ↗
40. Applications of drone in disaster management: A scoping review10.1016/j.scijus.2021.11.002 ↗
41. Detection of coal mine fires in the Jharia coal field using NOAA/AVHRR data10.1088/1742-2132/3/3/002 ↗
42. Fast and safe gas detection from underground coal fire by drone fly over10.1016/j.envpol.2017.05.063 ↗
43. Bamford, T., Esmaeili, K., and Schoellig, A.P. (2016). A real-time analysis of rock fragmentation using UAV technology. arXiv.10.1080/17480930.2017.1339170 ↗
44. Bamford, T., Esmaeili, K., and Schoellig, A.P. (2017). Aerial rock fragmentation analysis in low-light condition using UAV technology. arXiv.
45. Freire, G.R., and Cota, R.F. (2017, January 11–13). Capture of images in inaccessible areas in an underground mine using an unmanned aerial vehicle. Proceedings of the UMT 2017: Proceedings of the First International Conference on Underground Mining Technology, Australian Centre for Geomechanics, Sudbury, Canada.10.36487/ACG_rep/1710_54_Freire ↗
46. Castendyk, D.N., Straight, B.J., Voorhis, J.C., Somogyi, M.K., Jepson, W.E., and Kucera, B.L. (2019, January 3–5). Using aerial drones to select sample depths in pit lakes. Proceedings of the 13th International Conference on MINE Closure, Perth, WA, Australia.10.36487/ACG_rep/1915_89_Castendyk ↗
47. Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery10.1016/j.scitotenv.2018.12.156 ↗
48. Jackisch, R., Lorenz, S., Zimmermann, R., Möckel, R., and Gloaguen, R. (2018). Drone-borne hyperspectral monitoring of acid mine drainage: An example from the Sokolov lignite district. Remote Sens., 10.10.3390/rs10030385 ↗
49. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
50. ImageNet classification with deep convolutional neural networks
51. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18–23). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018, Salt Lake City, UT, USA.10.1109/CVPR.2018.00474 ↗
52. Perez, L., and Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification Using Deep Learning. arXiv.