View in EDS HTML Full Text PDF Full Text

Research on the efficient detection method of coal gangue based on multispectral imaging and knowledge distillation strategy of SCG-YOLO.

Saved in:
Bibliographic Details
Title: Research on the efficient detection method of coal gangue based on multispectral imaging and knowledge distillation strategy of SCG-YOLO.
Authors: Yan, Pengcheng1,2,3 (AUTHOR), Wu, Zhiqi2 (AUTHOR) 1326653938@qq.com, Wen, Ziming2 (AUTHOR), Zhao, Yuanjun2 (AUTHOR), Sun, Hao2 (AUTHOR), Wang, Pinghong2 (AUTHOR)
Source: International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 10, p3298-3326. 29p.
Subject Terms: *Multispectral imaging, *Distillation, *Object recognition (Computer vision), *Principal components analysis, *Embedded computer systems, *Detection algorithms
Abstract: With the aim of more effectively deploying the coal gangue detection model on embedded devices with limited resources, it is necessary to design smaller networks to reduce the number of parameters. By leveraging the complementarity of spectra across different bands, multispectral technology can fuse image information from various bands, effectively overcoming the environmental limitations of a single spectrum. This paper combines multispectral imaging technology and knowledge distillation to propose a lightweight Faster-YOLO coal gangue detection model. To begin with, multispectral imaging technology is applied to gather the spectral data of coal and gangue within diverse wavebands. Based on the spectral data of coal gangue in different bands, principal component analysis is conducted to reduce the dimensionality from 25 bands to 3 bands, and image fusion is implemented with these 3 principal component bands. Secondly, the YOLOv10n (You Only Look Once) target detection algorithm was improved. The improved SCG-YOLO was selected as the teacher model. By introducing SPD-Conv, the feature extraction ability of the network for low-resolution images was increased. A Neck structure based on CGRFPN was proposed. The enhanced SPD-Conv and CGRFPN are taken as the teacher model for knowledge distillation to diminish the model size and enhance the inference speed. Then, Faster-YOLO was selected as the student model. The network structure was made lightweight through the introduction of the FasterNet block. Through knowledge distillation, the network maintains good feature expression ability while reducing the amount of computation. The experimental results show that the Precision (P), Recall (R), and mean Average Precision (mAP) of the improved model after distillation for the recognition of coal and gangue are 97.4%, 96.1%, and 99.1%, respectively, which are 6%, 6.9%, and 2.3% higher than those of the original model, respectively. The number of parameters and the model size are 92.8% and 78.2% of those of the original model, respectively. The distilled Faster-YOLO algorithm has good detection accuracy, is lighter in weight, and is easily deployable on mobile terminals, which has a positive effect on the intelligent mining of underground coal mines. [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: 197620872
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Research on the efficient detection method of coal gangue based on multispectral imaging and knowledge distillation strategy of SCG-YOLO.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yan%2C+Pengcheng%22">Yan, Pengcheng</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Zhiqi%22">Wu, Zhiqi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 1326653938@qq.com</i><br /><searchLink fieldCode="AR" term="%22Wen%2C+Ziming%22">Wen, Ziming</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yuanjun%22">Zhao, Yuanjun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Hao%22">Sun, Hao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Pinghong%22">Wang, Pinghong</searchLink><relatesTo>2</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 10, p3298-3326. 29p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Multispectral+imaging%22">Multispectral imaging</searchLink><br />*<searchLink fieldCode="DE" term="%22Distillation%22">Distillation</searchLink><br />*<searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br />*<searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Embedded+computer+systems%22">Embedded computer systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the aim of more effectively deploying the coal gangue detection model on embedded devices with limited resources, it is necessary to design smaller networks to reduce the number of parameters. By leveraging the complementarity of spectra across different bands, multispectral technology can fuse image information from various bands, effectively overcoming the environmental limitations of a single spectrum. This paper combines multispectral imaging technology and knowledge distillation to propose a lightweight Faster-YOLO coal gangue detection model. To begin with, multispectral imaging technology is applied to gather the spectral data of coal and gangue within diverse wavebands. Based on the spectral data of coal gangue in different bands, principal component analysis is conducted to reduce the dimensionality from 25 bands to 3 bands, and image fusion is implemented with these 3 principal component bands. Secondly, the YOLOv10n (You Only Look Once) target detection algorithm was improved. The improved SCG-YOLO was selected as the teacher model. By introducing SPD-Conv, the feature extraction ability of the network for low-resolution images was increased. A Neck structure based on CGRFPN was proposed. The enhanced SPD-Conv and CGRFPN are taken as the teacher model for knowledge distillation to diminish the model size and enhance the inference speed. Then, Faster-YOLO was selected as the student model. The network structure was made lightweight through the introduction of the FasterNet block. Through knowledge distillation, the network maintains good feature expression ability while reducing the amount of computation. The experimental results show that the Precision (P), Recall (R), and mean Average Precision (mAP) of the improved model after distillation for the recognition of coal and gangue are 97.4%, 96.1%, and 99.1%, respectively, which are 6%, 6.9%, and 2.3% higher than those of the original model, respectively. The number of parameters and the model size are 92.8% and 78.2% of those of the original model, respectively. The distilled Faster-YOLO algorithm has good detection accuracy, is lighter in weight, and is easily deployable on mobile terminals, which has a positive effect on the intelligent mining of underground coal mines. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=197620872
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/19392699.2025.2575188
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 29
        StartPage: 3298
    Subjects:
      – SubjectFull: Multispectral imaging
        Type: general
      – SubjectFull: Distillation
        Type: general
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Embedded computer systems
        Type: general
      – SubjectFull: Detection algorithms
        Type: general
    Titles:
      – TitleFull: Research on the efficient detection method of coal gangue based on multispectral imaging and knowledge distillation strategy of SCG-YOLO.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yan, Pengcheng
      – PersonEntity:
          Name:
            NameFull: Wu, Zhiqi
      – PersonEntity:
          Name:
            NameFull: Wen, Ziming
      – PersonEntity:
          Name:
            NameFull: Zhao, Yuanjun
      – PersonEntity:
          Name:
            NameFull: Sun, Hao
      – PersonEntity:
          Name:
            NameFull: Wang, Pinghong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: 2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19392699
          Numbering:
            – Type: volume
              Value: 46
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: International Journal of Coal Preparation & Utilization
              Type: main
ResultId 1