Application of Deep Learning in Petrographic Coal Images Segmentation
The study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it behaves in various tech...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
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| Series: | Minerals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-163X/11/11/1265 |
| _version_ | 1797509242877378560 |
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| author | Sebastian Iwaszenko Leokadia Róg |
| author_facet | Sebastian Iwaszenko Leokadia Róg |
| author_sort | Sebastian Iwaszenko |
| collection | DOAJ |
| description | The study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it behaves in various technological processes. This paper considers the application of convolutional neural networks for coal petrographic images segmentation. The U-Net-based model for segmentation was proposed. The network was trained to segment inertinite, liptinite, and vitrinite. The segmentations prepared manually by a domain expert were used as the ground truth. The results show that inertinite and vitrinite can be successfully segmented with minimal difference from the ground truth. The liptinite turned out to be much more difficult to segment. After usage of transfer learning, moderate results were obtained. Nevertheless, the application of the U-Net-based network for petrographic image segmentation was successful. The results are good enough to consider the method as a supporting tool for domain experts in everyday work. |
| first_indexed | 2024-03-10T05:14:55Z |
| format | Article |
| id | doaj.art-90b906fa103447dfba663b97fbb55ce8 |
| institution | Directory Open Access Journal |
| issn | 2075-163X |
| language | English |
| last_indexed | 2024-03-10T05:14:55Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Minerals |
| spelling | doaj.art-90b906fa103447dfba663b97fbb55ce82023-11-23T00:32:46ZengMDPI AGMinerals2075-163X2021-11-011111126510.3390/min11111265Application of Deep Learning in Petrographic Coal Images SegmentationSebastian Iwaszenko0Leokadia Róg1Department of Acoustics, Electronics and IT Solutions, GIG Research Institute, 40-166 Katowice, PolandDepartment of Solid Fuels Quality Assessment, GIG Research Institute, 40-166 Katowice, PolandThe study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it behaves in various technological processes. This paper considers the application of convolutional neural networks for coal petrographic images segmentation. The U-Net-based model for segmentation was proposed. The network was trained to segment inertinite, liptinite, and vitrinite. The segmentations prepared manually by a domain expert were used as the ground truth. The results show that inertinite and vitrinite can be successfully segmented with minimal difference from the ground truth. The liptinite turned out to be much more difficult to segment. After usage of transfer learning, moderate results were obtained. Nevertheless, the application of the U-Net-based network for petrographic image segmentation was successful. The results are good enough to consider the method as a supporting tool for domain experts in everyday work.https://www.mdpi.com/2075-163X/11/11/1265coalpetrographic analysismaceralsimage analysissemantic segmentationconvolutional neural networks |
| spellingShingle | Sebastian Iwaszenko Leokadia Róg Application of Deep Learning in Petrographic Coal Images Segmentation Minerals coal petrographic analysis macerals image analysis semantic segmentation convolutional neural networks |
| title | Application of Deep Learning in Petrographic Coal Images Segmentation |
| title_full | Application of Deep Learning in Petrographic Coal Images Segmentation |
| title_fullStr | Application of Deep Learning in Petrographic Coal Images Segmentation |
| title_full_unstemmed | Application of Deep Learning in Petrographic Coal Images Segmentation |
| title_short | Application of Deep Learning in Petrographic Coal Images Segmentation |
| title_sort | application of deep learning in petrographic coal images segmentation |
| topic | coal petrographic analysis macerals image analysis semantic segmentation convolutional neural networks |
| url | https://www.mdpi.com/2075-163X/11/11/1265 |
| work_keys_str_mv | AT sebastianiwaszenko applicationofdeeplearninginpetrographiccoalimagessegmentation AT leokadiarog applicationofdeeplearninginpetrographiccoalimagessegmentation |