An edge extraction approach for hot forging images based on discrete grayscale surface properties.
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| Title: | An edge extraction approach for hot forging images based on discrete grayscale surface properties. |
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| Authors: | Pan, Xiaoyu1 (AUTHOR), Wang, Zhi1 (AUTHOR), Wang, Delun1 (AUTHOR) dlunwang@dlut.edu.cn |
| Source: | International Journal of Advanced Manufacturing Technology. Mar2025, Vol. 137 Issue 3, p1869-1890. 22p. |
| Subjects: | Stereo image processing, Computer vision, Grayscale model, Heat radiation & absorption, Surface properties |
| Abstract: | Machine vision measurement is desirable to permit real-time non-contact measuring and positioning for hot forgings, among which edge extraction is a most essential issue to extract the contour and effective area. However, conventional edge detection methods are prone to get unsatisfactory edging extraction results, thus have poor effectiveness, and are not suitable for hot forging images. In this paper, an efficient and robust edge extraction approach for passive vision images of hot forgings is proposed. Grayscale images of hot forgings converted into discrete gray surface, the approach is based on the geometric properties and the continuity of the equivalent discrete grayscale surface. The presented algorithm detects three types of edges by various continuity criterions, which are corresponded to the geometric properties and vary with the primary and secondary edges. The geometric properties dependent nature of the algorithm ensures the primary and the secondary edges of the forges are identified in the different environmental conditions and for forging parts with various heat radiation intensities. Moreover, an edge thinning and connection approach is presented by defining the edging direction, which can be used to improve the qualities of types of edges. Finally, experimentations for images of various sorts of hot forgings are carried out to extract three types of edges; the relevant experimental results and validation indicators show that the proposed method takes better performance as 17.4453 in PSNR and 0.1146 in entropy for G0 edge for a typical forging image while 0.0342 for G2 edge compared with existing methods. The results demonstrate that the proposed approach is validated to have satisfactory performance, as well as efficacy and robustness. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 183407411 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An edge extraction approach for hot forging images based on discrete grayscale surface properties. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pan%2C+Xiaoyu%22">Pan, Xiaoyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Zhi%22">Wang, Zhi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Delun%22">Wang, Delun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dlunwang@dlut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. Mar2025, Vol. 137 Issue 3, p1869-1890. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Stereo+image+processing%22">Stereo image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Grayscale+model%22">Grayscale model</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+radiation+%26+absorption%22">Heat radiation & absorption</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+properties%22">Surface properties</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Machine vision measurement is desirable to permit real-time non-contact measuring and positioning for hot forgings, among which edge extraction is a most essential issue to extract the contour and effective area. However, conventional edge detection methods are prone to get unsatisfactory edging extraction results, thus have poor effectiveness, and are not suitable for hot forging images. In this paper, an efficient and robust edge extraction approach for passive vision images of hot forgings is proposed. Grayscale images of hot forgings converted into discrete gray surface, the approach is based on the geometric properties and the continuity of the equivalent discrete grayscale surface. The presented algorithm detects three types of edges by various continuity criterions, which are corresponded to the geometric properties and vary with the primary and secondary edges. The geometric properties dependent nature of the algorithm ensures the primary and the secondary edges of the forges are identified in the different environmental conditions and for forging parts with various heat radiation intensities. Moreover, an edge thinning and connection approach is presented by defining the edging direction, which can be used to improve the qualities of types of edges. Finally, experimentations for images of various sorts of hot forgings are carried out to extract three types of edges; the relevant experimental results and validation indicators show that the proposed method takes better performance as 17.4453 in PSNR and 0.1146 in entropy for G0 edge for a typical forging image while 0.0342 for G2 edge compared with existing methods. The results demonstrate that the proposed approach is validated to have satisfactory performance, as well as efficacy and robustness. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00170-025-15214-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1869 Subjects: – SubjectFull: Stereo image processing Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Grayscale model Type: general – SubjectFull: Heat radiation & absorption Type: general – SubjectFull: Surface properties Type: general Titles: – TitleFull: An edge extraction approach for hot forging images based on discrete grayscale surface properties. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pan, Xiaoyu – PersonEntity: Name: NameFull: Wang, Zhi – PersonEntity: Name: NameFull: Wang, Delun IsPartOfRelationships: – BibEntity: Dates: – D: 21 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02683768 Numbering: – Type: volume Value: 137 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Advanced Manufacturing Technology Type: main |
| ResultId | 1 |
