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An improved cross algorithm edge detection method and its application in coal gangue identification.

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Title: An improved cross algorithm edge detection method and its application in coal gangue identification.
Authors: Wang, Xinquan1 (AUTHOR) wxq995@126.com, Zhao, Panpan1,2 (AUTHOR), Yue, Shengwei1 (AUTHOR), Gu, Yu1 (AUTHOR)
Source: International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 3, p835-847. 13p.
Subject Terms: *Edge detection (Image processing), *Image segmentation, *Machine learning, *Image processing, *Support vector machines, *Image recognition (Computer vision)
Abstract: In order to solve the problems of edge detection error, image segmentation distortion and low recognition accuracy in coal gangue separation under complex background conditions, an improved cross algorithm edge detection method is proposed in this paper, and it is applied in an example of coal gangue recognition. The cross algorithm is used to detect the edges of coal and coal gangue images, and the detection results are processed by morphological technology to obtain the segmentation results of single coal and coal gangue images. The mean value, contrast and entropy value of coal and coal gangue are extracted as recognition features to construct a support vector machine (SVM) recognition model. The accuracy of SVM identification model is 96.67% for coal and 100% for coal gangue, and it is obviously superior to K-Nearest Neighbor (KNN) identification model, Probabilistic Neural Network (PNN) identification model and Back Propagation Neural Network (BP) identification model in time. This study provides a new method to solve the identification problem of coal and gangue. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 192006239
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: An improved cross algorithm edge detection method and its application in coal gangue identification.
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  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Xinquan%22">Wang, Xinquan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wxq995@126.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Panpan%22">Zhao, Panpan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yue%2C+Shengwei%22">Yue, Shengwei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gu%2C+Yu%22">Gu, Yu</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Coal+Preparation+%26+Utilization%22">International Journal of Coal Preparation & Utilization</searchLink>. 2026, Vol. 46 Issue 3, p835-847. 13p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Edge+detection+%28Image+processing%29%22">Edge detection (Image processing)</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In order to solve the problems of edge detection error, image segmentation distortion and low recognition accuracy in coal gangue separation under complex background conditions, an improved cross algorithm edge detection method is proposed in this paper, and it is applied in an example of coal gangue recognition. The cross algorithm is used to detect the edges of coal and coal gangue images, and the detection results are processed by morphological technology to obtain the segmentation results of single coal and coal gangue images. The mean value, contrast and entropy value of coal and coal gangue are extracted as recognition features to construct a support vector machine (SVM) recognition model. The accuracy of SVM identification model is 96.67% for coal and 100% for coal gangue, and it is obviously superior to K-Nearest Neighbor (KNN) identification model, Probabilistic Neural Network (PNN) identification model and Back Propagation Neural Network (BP) identification model in time. This study provides a new method to solve the identification problem of coal and gangue. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192006239
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/19392699.2025.2481147
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 835
    Subjects:
      – SubjectFull: Edge detection (Image processing)
        Type: general
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Image recognition (Computer vision)
        Type: general
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      – TitleFull: An improved cross algorithm edge detection method and its application in coal gangue identification.
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            NameFull: Zhao, Panpan
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            NameFull: Yue, Shengwei
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            – D: 01
              M: 03
              Text: 2026
              Type: published
              Y: 2026
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              Value: 46
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            – TitleFull: International Journal of Coal Preparation & Utilization
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