An automatic segmentation method for coal gangue based on improved region growing algorithm.
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| Title: | An automatic segmentation method for coal gangue based on improved region growing algorithm. |
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| Authors: | Li, Donghui1 (AUTHOR), Wu, Hao1 (AUTHOR), Chen, Kaiyun1 (AUTHOR) chenkaiyun@usth.edu.cn, Wang, Yanwei1 (AUTHOR) |
| Source: | International Journal of Coal Preparation & Utilization. 2026, Vol. 46 Issue 7, p1992-2017. 26p. |
| Subject Terms: | *Image segmentation, *Algorithms, *Coal, *Manipulators (Machinery), *Image processing |
| Abstract: | Accurate segmentation of coal gangue contours during intelligent coal gangue sorting substantially reduces the occurrence of coal gangue dropping and misgrasping by robotic manipulators, thus enhancing sorting efficiency. Precise segmentation using computer algorithms effectively extracts coal gangue contours, thereby enhancing intelligent sorting efficiency. The key to achieving accurate segmentation lies in enhancing algorithmic performance. Therefore, we propose an improved region growing algorithm for automatic coal gangue segmentation. This algorithm introduces improvements in coarse contour acquisition, seed point expansion mechanisms, and automatic threshold updating. The final segmentation result is achieved through the combination of multiple segmentation outcomes. We conducted 20 experiments to evaluate the performance of the improved region growing algorithm, comparing it with four used segmentation algorithms. Experimental results show that the proposed algorithm achieved an average Dice coefficient of 0.988 and an average Jaccard distance of 0.023. These findings demonstrate that the proposed algorithm can automatically segment coal gangue contours with high accuracy and robustness. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 194897997 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An automatic segmentation method for coal gangue based on improved region growing algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Donghui%22">Li, Donghui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Hao%22">Wu, Hao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Kaiyun%22">Chen, Kaiyun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chenkaiyun@usth.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yanwei%22">Wang, Yanwei</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 7, p1992-2017. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Coal%22">Coal</searchLink><br />*<searchLink fieldCode="DE" term="%22Manipulators+%28Machinery%29%22">Manipulators (Machinery)</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurate segmentation of coal gangue contours during intelligent coal gangue sorting substantially reduces the occurrence of coal gangue dropping and misgrasping by robotic manipulators, thus enhancing sorting efficiency. Precise segmentation using computer algorithms effectively extracts coal gangue contours, thereby enhancing intelligent sorting efficiency. The key to achieving accurate segmentation lies in enhancing algorithmic performance. Therefore, we propose an improved region growing algorithm for automatic coal gangue segmentation. This algorithm introduces improvements in coarse contour acquisition, seed point expansion mechanisms, and automatic threshold updating. The final segmentation result is achieved through the combination of multiple segmentation outcomes. We conducted 20 experiments to evaluate the performance of the improved region growing algorithm, comparing it with four used segmentation algorithms. Experimental results show that the proposed algorithm achieved an average Dice coefficient of 0.988 and an average Jaccard distance of 0.023. These findings demonstrate that the proposed algorithm can automatically segment coal gangue contours with high accuracy and robustness. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194897997 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/19392699.2025.2520985 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 1992 Subjects: – SubjectFull: Image segmentation Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Coal Type: general – SubjectFull: Manipulators (Machinery) Type: general – SubjectFull: Image processing Type: general Titles: – TitleFull: An automatic segmentation method for coal gangue based on improved region growing algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Donghui – PersonEntity: Name: NameFull: Wu, Hao – PersonEntity: Name: NameFull: Chen, Kaiyun – PersonEntity: Name: NameFull: Wang, Yanwei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19392699 Numbering: – Type: volume Value: 46 – Type: issue Value: 7 Titles: – TitleFull: International Journal of Coal Preparation & Utilization Type: main |
| ResultId | 1 |
