Weld Seam Identification Using Edge Detection in Machine Vision.
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| Title: | Weld Seam Identification Using Edge Detection in Machine Vision. |
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| Authors: | ZHAO, Chenlei1 zhaochenlei@stu.xhu.edu.cn, WU, Dong1 447325098@qq.com, XI, Lin1 lishang_1880@163.com, GUO, Lihong2 guo_lihong123@126.com, WU, Shenghong3 1529432209@qq.com, LUO, Xiao4 535292271@qq.com, HU, Shunyang5 kojunyou@gmail.com, DING, Yiran6 3180900037@qq.com |
| Source: | Technical Gazette / Tehnički Vjesnik. 2026, Vol. 33 Issue 3, p1244-1252. 9p. |
| Subjects: | Edge detection (Image processing), Welding inspection, Image enhancement (Imaging systems), MatLab (Computer software), Computer vision, Image processing |
| Abstract: | In intelligent welding, achieving the automation of weld quality inspection is a significant challenge, and weld seam marking is of crucial importance. For this purpose, a method based on edge detection using binary image preprocessing was developed on the MATLAB platform. Compared with the traditional multi-sensor fusion approach, this method does not require complex sensor integration, simplifying the implementation process. Compared with neural network methods, it is more flexible and simpler. The method first preprocesses the image into a binary image and then compares the weld seam feature marking with the Roberts, Prewitt, Sobel, and Canny operators. The results show that the Canny operator demonstrates a significant performance advantage in the comparison of four indicators: point sharpness, entropy, average gradient, and Quality Assessment of Blended Features. Its performance is 3 to 25 times that of other operators, and it performs best in weld seam feature texture detection, showing high robustness. [ABSTRACT FROM AUTHOR] |
| Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195131811 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Weld Seam Identification Using Edge Detection in Machine Vision. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22ZHAO%2C+Chenlei%22">ZHAO, Chenlei</searchLink><relatesTo>1</relatesTo><i> zhaochenlei@stu.xhu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22WU%2C+Dong%22">WU, Dong</searchLink><relatesTo>1</relatesTo><i> 447325098@qq.com</i><br /><searchLink fieldCode="AR" term="%22XI%2C+Lin%22">XI, Lin</searchLink><relatesTo>1</relatesTo><i> lishang_1880@163.com</i><br /><searchLink fieldCode="AR" term="%22GUO%2C+Lihong%22">GUO, Lihong</searchLink><relatesTo>2</relatesTo><i> guo_lihong123@126.com</i><br /><searchLink fieldCode="AR" term="%22WU%2C+Shenghong%22">WU, Shenghong</searchLink><relatesTo>3</relatesTo><i> 1529432209@qq.com</i><br /><searchLink fieldCode="AR" term="%22LUO%2C+Xiao%22">LUO, Xiao</searchLink><relatesTo>4</relatesTo><i> 535292271@qq.com</i><br /><searchLink fieldCode="AR" term="%22HU%2C+Shunyang%22">HU, Shunyang</searchLink><relatesTo>5</relatesTo><i> kojunyou@gmail.com</i><br /><searchLink fieldCode="AR" term="%22DING%2C+Yiran%22">DING, Yiran</searchLink><relatesTo>6</relatesTo><i> 3180900037@qq.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technical+Gazette+%2F+Tehnički+Vjesnik%22">Technical Gazette / Tehnički Vjesnik</searchLink>. 2026, Vol. 33 Issue 3, p1244-1252. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Edge+detection+%28Image+processing%29%22">Edge detection (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Welding+inspection%22">Welding inspection</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22MatLab+%28Computer+software%29%22">MatLab (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In intelligent welding, achieving the automation of weld quality inspection is a significant challenge, and weld seam marking is of crucial importance. For this purpose, a method based on edge detection using binary image preprocessing was developed on the MATLAB platform. Compared with the traditional multi-sensor fusion approach, this method does not require complex sensor integration, simplifying the implementation process. Compared with neural network methods, it is more flexible and simpler. The method first preprocesses the image into a binary image and then compares the weld seam feature marking with the Roberts, Prewitt, Sobel, and Canny operators. The results show that the Canny operator demonstrates a significant performance advantage in the comparison of four indicators: point sharpness, entropy, average gradient, and Quality Assessment of Blended Features. Its performance is 3 to 25 times that of other operators, and it performs best in weld seam feature texture detection, showing high robustness. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technical Gazette / Tehnički Vjesnik is the property of Tehnicki Vjesnik 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: 10.17559/TV-20250612002743 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1244 Subjects: – SubjectFull: Edge detection (Image processing) Type: general – SubjectFull: Welding inspection Type: general – SubjectFull: Image enhancement (Imaging systems) Type: general – SubjectFull: MatLab (Computer software) Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Image processing Type: general Titles: – TitleFull: Weld Seam Identification Using Edge Detection in Machine Vision. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: ZHAO, Chenlei – PersonEntity: Name: NameFull: WU, Dong – PersonEntity: Name: NameFull: XI, Lin – PersonEntity: Name: NameFull: GUO, Lihong – PersonEntity: Name: NameFull: WU, Shenghong – PersonEntity: Name: NameFull: LUO, Xiao – PersonEntity: Name: NameFull: HU, Shunyang – PersonEntity: Name: NameFull: DING, Yiran IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13303651 Numbering: – Type: volume Value: 33 – Type: issue Value: 3 Titles: – TitleFull: Technical Gazette / Tehnički Vjesnik Type: main |
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