Attention U-Net-based semantic segmentation for welding line detection
Abstract In industrial processes, quality assurance through methods such as visual inspection is essential for ensuring process stability. Traditional manual visual inspection is a time-consuming and costly endeavor. If the opportunity arises, replacing manual visual inspection with AI could lead to...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-00257-2 |
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| author | Hunor István Lukács Bence Zsolt Beregi Balázs Porteleki Tamás Fischl János Botzheim |
| author_facet | Hunor István Lukács Bence Zsolt Beregi Balázs Porteleki Tamás Fischl János Botzheim |
| author_sort | Hunor István Lukács |
| collection | DOAJ |
| description | Abstract In industrial processes, quality assurance through methods such as visual inspection is essential for ensuring process stability. Traditional manual visual inspection is a time-consuming and costly endeavor. If the opportunity arises, replacing manual visual inspection with AI could lead to significant efficiency gains. However, simply judging the correctness or incorrectness of a process is often insufficient; quantitative attributes must also be associated with visual inspection. This paper proposes a solution for replacing manual visual inspection with AI specifically for welded joints. The aim is not only to detect the presence of weld joints but also to assess their geometric dimensions. Leveraging a proposed Attention U-Net architecture in combination with rule-based metrics, the proposed method offers a novel solution for identifying welding lines in images. By integrating semantic segmentation techniques, the method effectively distinguishes weld joint elements, while rule-based metrics facilitate the identification of critical cases requiring human intervention. Experimental results demonstrate the method’s capability to automate a significant portion of inspection tasks, thereby reducing the reliance on manual labor and enhancing overall process efficiency and reliability. |
| format | Article |
| id | doaj-art-79a69dbfc9b34f20b2b05de8969744e4 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-79a69dbfc9b34f20b2b05de8969744e42025-08-20T02:10:56ZengNature PortfolioScientific Reports2045-23222025-05-0115111110.1038/s41598-025-00257-2Attention U-Net-based semantic segmentation for welding line detectionHunor István Lukács0Bence Zsolt Beregi1Balázs Porteleki2Tamás Fischl3János Botzheim4Department of Artificial Intelligence, Faculty of Informatics, ELTE Eötvös Loránd UniversityDepartment of Artificial Intelligence, Faculty of Informatics, ELTE Eötvös Loránd UniversityRobert Bosch KftRobert Bosch KftDepartment of Artificial Intelligence, Faculty of Informatics, ELTE Eötvös Loránd UniversityAbstract In industrial processes, quality assurance through methods such as visual inspection is essential for ensuring process stability. Traditional manual visual inspection is a time-consuming and costly endeavor. If the opportunity arises, replacing manual visual inspection with AI could lead to significant efficiency gains. However, simply judging the correctness or incorrectness of a process is often insufficient; quantitative attributes must also be associated with visual inspection. This paper proposes a solution for replacing manual visual inspection with AI specifically for welded joints. The aim is not only to detect the presence of weld joints but also to assess their geometric dimensions. Leveraging a proposed Attention U-Net architecture in combination with rule-based metrics, the proposed method offers a novel solution for identifying welding lines in images. By integrating semantic segmentation techniques, the method effectively distinguishes weld joint elements, while rule-based metrics facilitate the identification of critical cases requiring human intervention. Experimental results demonstrate the method’s capability to automate a significant portion of inspection tasks, thereby reducing the reliance on manual labor and enhancing overall process efficiency and reliability.https://doi.org/10.1038/s41598-025-00257-2Weld detectionAttention U-NetSemantic segmentationIndustrial AIAI-based automatizationMachine vision |
| spellingShingle | Hunor István Lukács Bence Zsolt Beregi Balázs Porteleki Tamás Fischl János Botzheim Attention U-Net-based semantic segmentation for welding line detection Scientific Reports Weld detection Attention U-Net Semantic segmentation Industrial AI AI-based automatization Machine vision |
| title | Attention U-Net-based semantic segmentation for welding line detection |
| title_full | Attention U-Net-based semantic segmentation for welding line detection |
| title_fullStr | Attention U-Net-based semantic segmentation for welding line detection |
| title_full_unstemmed | Attention U-Net-based semantic segmentation for welding line detection |
| title_short | Attention U-Net-based semantic segmentation for welding line detection |
| title_sort | attention u net based semantic segmentation for welding line detection |
| topic | Weld detection Attention U-Net Semantic segmentation Industrial AI AI-based automatization Machine vision |
| url | https://doi.org/10.1038/s41598-025-00257-2 |
| work_keys_str_mv | AT hunoristvanlukacs attentionunetbasedsemanticsegmentationforweldinglinedetection AT bencezsoltberegi attentionunetbasedsemanticsegmentationforweldinglinedetection AT balazsporteleki attentionunetbasedsemanticsegmentationforweldinglinedetection AT tamasfischl attentionunetbasedsemanticsegmentationforweldinglinedetection AT janosbotzheim attentionunetbasedsemanticsegmentationforweldinglinedetection |