Detection of surface rill erosion on tailings dams using aerial images and deep neural network APCOM 2021

The Southern African Institute of Mining and Metallurgy
F. F. Nasategay E. E. Gohari R. Battulwar M. Z. Naghadehi J. Sattarvand
Organization:
The Southern African Institute of Mining and Metallurgy
Pages:
10
File Size:
1824 KB
Publication Date:
Sep 1, 2021

Abstract

After the recent increase in catastrophic failures of tailings dams such as the Burmadinho, Brazil failure in 2019, the mining industry has actively engaged in the stabilisation of tailings dams. Most of the tailings storage facility (TSF) guidelines suggest a laborious action that can be facilitated through imaging by unmanned aerial vehicles (UAVs). Although UAVs are not unknown to most mining operations, automated rill detection through aerial image processing is a new concept. This study utilises images from sites in the U.S. and employs semantic segmentation with a UNet architecture to detect location and size of these rills. The developed model can be used as a tool for monitoring surface erosion of TSFs.
Citation

APA: F. F. Nasategay E. E. Gohari R. Battulwar M. Z. Naghadehi J. Sattarvand  (2021)  Detection of surface rill erosion on tailings dams using aerial images and deep neural network APCOM 2021

MLA: F. F. Nasategay E. E. Gohari R. Battulwar M. Z. Naghadehi J. Sattarvand Detection of surface rill erosion on tailings dams using aerial images and deep neural network APCOM 2021. The Southern African Institute of Mining and Metallurgy, 2021.

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