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An Ore Image Segmentation Method Based on RDU-Net Model
The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmenta...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506798/ https://www.ncbi.nlm.nih.gov/pubmed/32887432 http://dx.doi.org/10.3390/s20174979 |
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author | Xiao, Dong Liu, Xiwen Le, Ba Tuan Ji, Zhiwen Sun, Xiaoyu |
author_facet | Xiao, Dong Liu, Xiwen Le, Ba Tuan Ji, Zhiwen Sun, Xiaoyu |
author_sort | Xiao, Dong |
collection | PubMed |
description | The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator. |
format | Online Article Text |
id | pubmed-7506798 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75067982020-09-26 An Ore Image Segmentation Method Based on RDU-Net Model Xiao, Dong Liu, Xiwen Le, Ba Tuan Ji, Zhiwen Sun, Xiaoyu Sensors (Basel) Article The ore fragment size on the conveyor belt of concentrators is not only the main index to verify the crushing process, but also affects the production efficiency, operation cost and even production safety of the mine. In order to get the size of ore fragments on the conveyor belt, the image segmentation method is a convenient and fast choice. However, due to the influence of dust, light and uneven color and texture, the traditional ore image segmentation methods are prone to oversegmentation and undersegmentation. In order to solve these problems, this paper proposes an ore image segmentation model called RDU-Net (R: residual connection; DU: DUNet), which combines the residual structure of convolutional neural network with DUNet model, greatly improving the accuracy of image segmentation. RDU-Net can adaptively adjust the receptive field according to the size and shape of different ore fragments, capture the ore edge of different shape and size, and realize the accurate segmentation of ore image. The experimental results show that compared with other U-Net and DUNet, the RDU-Net has significantly improved segmentation accuracy, and has better generalization ability, which can fully meet the requirements of ore fragment size detection in the concentrator. MDPI 2020-09-02 /pmc/articles/PMC7506798/ /pubmed/32887432 http://dx.doi.org/10.3390/s20174979 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xiao, Dong Liu, Xiwen Le, Ba Tuan Ji, Zhiwen Sun, Xiaoyu An Ore Image Segmentation Method Based on RDU-Net Model |
title | An Ore Image Segmentation Method Based on RDU-Net Model |
title_full | An Ore Image Segmentation Method Based on RDU-Net Model |
title_fullStr | An Ore Image Segmentation Method Based on RDU-Net Model |
title_full_unstemmed | An Ore Image Segmentation Method Based on RDU-Net Model |
title_short | An Ore Image Segmentation Method Based on RDU-Net Model |
title_sort | ore image segmentation method based on rdu-net model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506798/ https://www.ncbi.nlm.nih.gov/pubmed/32887432 http://dx.doi.org/10.3390/s20174979 |
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