View in EDS HTML Full Text PDF Full Text

Improved mask R-CNN-based instance segmentation model for coal gangue.

Saved in:
Bibliographic Details
Title: Improved mask R-CNN-based instance segmentation model for coal gangue.
Authors: Sun, Peibo1 (AUTHOR) 1592984862@qq.com, Wu, Weimin1 (AUTHOR)
Source: International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 6, p1601-1619. 19p.
Subject Terms: *Image segmentation, *Coal mine waste, *Feature extraction
Abstract: To achieve rapid sorting of coal gangue in mining environments, we propose an efficient model, RepSortNet, based on an improved Mask R-CNN to enhance the accuracy and reliability of coal gangue instance segmentation. First, to address the insufficient feature extraction caused by low contrast between coal and gangue, we construct an advanced backbone, RepViT-SG, which enhances the extraction of deep semantic features while maintaining real-time performance. Second, to improve the model's adaptability to gangue at various scales, we design the BiFPN-EMA module, which integrates multi-scale feature fusion with multi-level attention mechanisms, enhancing the model's ability to perceive critical information across channels. Finally, by incorporating the dynamic weight update strategy of the DyHead module, the model effectively enhances its generalization performance for detecting multi-appearance gangue in complex mining environments characterized by dynamic lighting, occlusion, and morphologically diverse targets. Experimental results demonstrate that the proposed model achieves an accuracy of 81.7% bbox_AP and 76.1% segm_AP on a self-constructed coal gangue dataset with a processing speed of 58 FPS, achieving a trade-off between detection speed and accuracy. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 194221854
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Improved mask R-CNN-based instance segmentation model for coal gangue.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Sun%2C+Peibo%22">Sun, Peibo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 1592984862@qq.com</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Weimin%22">Wu, Weimin</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Coal+Preparation+%26+Utilization%22">International Journal of Coal Preparation & Utilization</searchLink>. 2026, Vol. 46 Issue 6, p1601-1619. 19p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br />*<searchLink fieldCode="DE" term="%22Coal+mine+waste%22">Coal mine waste</searchLink><br />*<searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To achieve rapid sorting of coal gangue in mining environments, we propose an efficient model, RepSortNet, based on an improved Mask R-CNN to enhance the accuracy and reliability of coal gangue instance segmentation. First, to address the insufficient feature extraction caused by low contrast between coal and gangue, we construct an advanced backbone, RepViT-SG, which enhances the extraction of deep semantic features while maintaining real-time performance. Second, to improve the model's adaptability to gangue at various scales, we design the BiFPN-EMA module, which integrates multi-scale feature fusion with multi-level attention mechanisms, enhancing the model's ability to perceive critical information across channels. Finally, by incorporating the dynamic weight update strategy of the DyHead module, the model effectively enhances its generalization performance for detecting multi-appearance gangue in complex mining environments characterized by dynamic lighting, occlusion, and morphologically diverse targets. Experimental results demonstrate that the proposed model achieves an accuracy of 81.7% bbox_AP and 76.1% segm_AP on a self-constructed coal gangue dataset with a processing speed of 58 FPS, achieving a trade-off between detection speed and accuracy. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194221854
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/19392699.2025.2505448
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 1601
    Subjects:
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Coal mine waste
        Type: general
      – SubjectFull: Feature extraction
        Type: general
    Titles:
      – TitleFull: Improved mask R-CNN-based instance segmentation model for coal gangue.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Sun, Peibo
      – PersonEntity:
          Name:
            NameFull: Wu, Weimin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: 2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19392699
          Numbering:
            – Type: volume
              Value: 46
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: International Journal of Coal Preparation & Utilization
              Type: main
ResultId 1