An Improved Boundary-Aware U-Net for Ore Image Semantic Segmentation Article Swipe
Wei Wang
,
Qing Li
,
Chengyong Xiao
,
Dezheng Zhang
,
Lei Miao
,
Lijun Wang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21082615
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/s21082615
Particle size is the most important index to reflect the crushing quality of ores, and the accuracy of particle size statistics directly affects the subsequent operation of mines. Accurate ore image segmentation is an important prerequisite to ensure the reliability of particle size statistics. However, given the diversity of the size and shape of ores, the influence of dust and light, the complex texture and shadows on the ore surface, and especially the adhesion between ores, it is difficult to segment ore images accurately, and under-segmentation can be a serious problem. The construction of a large, labeled dataset for complex and unclear conveyor belt ore images is also difficult. In response to these challenges, we propose a novel, multi-task learning network based on U-Net for ore image segmentation. To solve the problem of limited available training datasets and to improve the feature extraction ability of the model, an improved encoder based on Resnet18 is proposed. Different from the original U-Net, our model decoder includes a boundary subnetwork for boundary detection and a mask subnetwork for mask segmentation, and information of the two subnetworks is fused in a boundary mask fusion block (BMFB). The experimental results showed that the pixel accuracy, Intersection over Union (IOU) for the ore mask (IOU_M), IOU for the ore boundary (IOU_B), and error of the average statistical ore particle size (ASE) rate of our proposed model on the testing dataset were 92.07%, 86.95%, 52.32%, and 20.38%, respectively. Compared to the benchmark U-Net, the improvements were 0.65%, 1.01%, 5.78%, and 12.11% (down), respectively.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s21082615
- https://www.mdpi.com/1424-8220/21/8/2615/pdf?version=1617941487
- OA Status
- gold
- Cited By
- 45
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3142084776
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3142084776Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s21082615Digital Object Identifier
- Title
-
An Improved Boundary-Aware U-Net for Ore Image Semantic SegmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-08Full publication date if available
- Authors
-
Wei Wang, Qing Li, Chengyong Xiao, Dezheng Zhang, Lei Miao, Lijun WangList of authors in order
- Landing page
-
https://doi.org/10.3390/s21082615Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/21/8/2615/pdf?version=1617941487Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/21/8/2615/pdf?version=1617941487Direct OA link when available
- Concepts
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Boundary (topology), Artificial intelligence, Segmentation, Benchmark (surveying), Computer science, Pixel, Subnetwork, Image segmentation, Pattern recognition (psychology), Computer vision, Mathematics, Geology, Geodesy, Computer security, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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45Total citation count in OpenAlex
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2025: 8, 2024: 8, 2023: 18, 2022: 8, 2021: 3Per-year citation counts (last 5 years)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.be | 87 |
| abstract_inverted_index.in | 185 |
| abstract_inverted_index.is | 2, 32, 77, 106, 153, 183 |
| abstract_inverted_index.it | 76 |
| abstract_inverted_index.of | 12, 17, 26, 40, 48, 53, 57, 93, 132, 144, 179, 217, 226 |
| abstract_inverted_index.on | 66, 122, 151, 230 |
| abstract_inverted_index.to | 7, 36, 79, 111, 138, 242 |
| abstract_inverted_index.we | 114 |
| abstract_inverted_index.IOU | 209 |
| abstract_inverted_index.The | 91, 192 |
| abstract_inverted_index.and | 14, 51, 59, 64, 70, 84, 100, 137, 170, 177, 215, 238, 252 |
| abstract_inverted_index.can | 86 |
| abstract_inverted_index.for | 98, 124, 167, 174, 204, 210 |
| abstract_inverted_index.ore | 29, 68, 81, 104, 125, 206, 212, 221 |
| abstract_inverted_index.our | 160, 227 |
| abstract_inverted_index.the | 3, 9, 15, 23, 38, 46, 49, 55, 61, 67, 72, 130, 140, 145, 157, 180, 197, 205, 211, 218, 231, 243, 246 |
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| abstract_inverted_index.(ASE) | 224 |
| abstract_inverted_index.(IOU) | 203 |
| abstract_inverted_index.U-Net | 123 |
| abstract_inverted_index.Union | 202 |
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| abstract_inverted_index.these | 112 |
| abstract_inverted_index.0.65%, | 249 |
| abstract_inverted_index.1.01%, | 250 |
| abstract_inverted_index.12.11% | 253 |
| abstract_inverted_index.5.78%, | 251 |
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| abstract_inverted_index.ensure | 37 |
| abstract_inverted_index.fusion | 189 |
| abstract_inverted_index.images | 82, 105 |
| abstract_inverted_index.large, | 95 |
| abstract_inverted_index.light, | 60 |
| abstract_inverted_index.mines. | 27 |
| abstract_inverted_index.model, | 146 |
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| abstract_inverted_index.showed | 195 |
| abstract_inverted_index.(BMFB). | 191 |
| abstract_inverted_index.(down), | 254 |
| abstract_inverted_index.20.38%, | 239 |
| abstract_inverted_index.52.32%, | 237 |
| abstract_inverted_index.86.95%, | 236 |
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| abstract_inverted_index.Different | 155 |
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