research-article

Measure Dry Beach Length of Tailings Pond Using Deep Learning Algorithm

Authors:

Zhiyu QianAuthors Info & Claims

RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

Pages 503 - 508

https://doi.org/10.1145/3366194.3366284

Published: 20 September 2019 Publication History

Abstract

The length of dry beach determines the safety and stability of tailings dam. In order to measure the dry beach length more accurately, we put forward a method of measuring the dry beach length of tailings dam based on deep learning. This method is carried out in three steps:(1) installing monitoring cameras on both sides of tailings dam. (2) training the network model based on Mask R-CNN algorithm, identify waterline and outputs waterline coordinates. (3) measuring the length of dry beach by video screen in real time through inputting the waterline coordinates into the functional relationship between waterline coordinates and measured values. The results show that this model can accurately measure the length of dry beach, and is suitable for the conditions of insufficient illumination, blurred image, rain and snow.

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Cited By

Wang KZhang ZYang XWang DZhu LYuan S(2024)Enhanced Tailings Dam Beach Line Indicator Observation and Stability Numerical Analysis: An Approach Integrating UAV Photogrammetry and CNNsRemote Sensing10.3390/rs1617326416:17(3264)Online publication date: 3-Sep-2024
https://doi.org/10.3390/rs16173264
Pacheco FHermosilla GPiña OVillavicencio GAllende-Cid HPalma JValenzuela PGarcía JCarpanetti AMinatogawa VSuazo GLeón ALópez RNovoa G(2022)Generation of Synthetic Data for the Analysis of the Physical Stability of Tailing Dams through Artificial IntelligenceMathematics10.3390/math1023439610:23(4396)Online publication date: 22-Nov-2022
https://doi.org/10.3390/math10234396
Wang WLi LWang XLi QJia STong R(2022)Saturation Line Forecasting via a Channel and Temporal Attention-Based NetworkIEEE Access10.1109/ACCESS.2022.322281710(121641-121652)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3222817
Show More Cited By

Index Terms

Measure Dry Beach Length of Tailings Pond Using Deep Learning Algorithm
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Other conferences

RICAI '19: Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

September 2019

803 pages

ISBN:9781450372985

DOI:10.1145/3366194

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 20 September 2019

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RICAI 2019

RICAI 2019: 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence

September 20 - 22, 2019

Shanghai, China

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RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;

Overall Acceptance Rate 140 of 294 submissions, 48%

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Cited By

Wang KZhang ZYang XWang DZhu LYuan S(2024)Enhanced Tailings Dam Beach Line Indicator Observation and Stability Numerical Analysis: An Approach Integrating UAV Photogrammetry and CNNsRemote Sensing10.3390/rs1617326416:17(3264)Online publication date: 3-Sep-2024
https://doi.org/10.3390/rs16173264
Pacheco FHermosilla GPiña OVillavicencio GAllende-Cid HPalma JValenzuela PGarcía JCarpanetti AMinatogawa VSuazo GLeón ALópez RNovoa G(2022)Generation of Synthetic Data for the Analysis of the Physical Stability of Tailing Dams through Artificial IntelligenceMathematics10.3390/math1023439610:23(4396)Online publication date: 22-Nov-2022
https://doi.org/10.3390/math10234396
Wang WLi LWang XLi QJia STong R(2022)Saturation Line Forecasting via a Channel and Temporal Attention-Based NetworkIEEE Access10.1109/ACCESS.2022.322281710(121641-121652)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3222817
Yang JSun YLi QSun Y(2020)Effective Risk Prediction of Tailings Ponds Using Machine Learning2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)10.1109/AEMCSE50948.2020.00057(234-238)Online publication date: Apr-2020
https://doi.org/10.1109/AEMCSE50948.2020.00057
Yang JQu JMi QLi Q(2020)A CNN-LSTM Model for Tailings Dam Risk PredictionIEEE Access10.1109/ACCESS.2020.30379358(206491-206502)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3037935

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