High resolution weld semantic defect detection algorithm based on integrated double U structure
Abstract As a key industrial equipment, the welding quality of pressure vessels is directly related to the operation safety, and X-ray nondestructive testing is an important means to evaluate the weld quality. In this study, a high-resolution semantic segmentation algorithm based on a double U-shape...
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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/s41598-025-02421-0 |
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| author | Xiaoyan Li Yi Wei Zhigang Lv Peng Wang Liangliang Li Mengyu Sun Chu Wang |
| author_facet | Xiaoyan Li Yi Wei Zhigang Lv Peng Wang Liangliang Li Mengyu Sun Chu Wang |
| author_sort | Xiaoyan Li |
| collection | DOAJ |
| description | Abstract As a key industrial equipment, the welding quality of pressure vessels is directly related to the operation safety, and X-ray nondestructive testing is an important means to evaluate the weld quality. In this study, a high-resolution semantic segmentation algorithm based on a double U-shaped network was proposed to meet the requirements of pressure vessel weld defect detection, and accurate detection was realized through innovative three-stage processing flow. Firstly, the weld area was automatically extracted from the original X-ray films (3000+×800 + pixels) by the Gaussian positioning algorithm. Then, a multi-image hybrid stitching technology was proposed to reconstruct the long weld into a standard size of 1500 × 1500, which not only expanded the WSCR data set, but also effectively improved the data imbalance problem. In terms of model architecture, by improving U2Netp and UNet networks, MC-SPP module (multi-connection spatial pyramid pooling), RMAG module (residual multi-add gating recurrent unit), HDC-CBAM module (hybrid dilated convolution-convolutional block attention) and CCM module (cross-layer connection fusion) were integrated to form a cascade network with multi-level feature fusion capability. The experimental results showed that the model could effectively segment the defects such as cracks, pores, slag inclusion, incomplete fusion and incomplete penetration in the weld film, and the mIOU value reached 78.1, which was significantly superior to the existing methods, and provided an efficient and reliable solution for welding defect detection of pressure vessels, which had important engineering application value. |
| format | Article |
| id | doaj-art-a485709e2aba444391d2eb0648a5e4d5 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a485709e2aba444391d2eb0648a5e4d52025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111810.1038/s41598-025-02421-0High resolution weld semantic defect detection algorithm based on integrated double U structureXiaoyan Li0Yi Wei1Zhigang Lv2Peng Wang3Liangliang Li4Mengyu Sun5Chu Wang6School of Electronics and Information Engineering, Xi’an Technological UniversitySchool of Electronics and Information Engineering, Xi’an Technological UniversitySchool of Electronics and Information Engineering, Xi’an Technological UniversitySchool of Institute for Interdisciplinary and Innovation Research, Xi’an Technological UniversitySchool of Institute for Interdisciplinary and Innovation Research, Xi’an Technological UniversitySchool of Opto-electronical Engineering, Xi’an Technological UniversitySchool of Electronics and Information Engineering, Xi’an Technological UniversityAbstract As a key industrial equipment, the welding quality of pressure vessels is directly related to the operation safety, and X-ray nondestructive testing is an important means to evaluate the weld quality. In this study, a high-resolution semantic segmentation algorithm based on a double U-shaped network was proposed to meet the requirements of pressure vessel weld defect detection, and accurate detection was realized through innovative three-stage processing flow. Firstly, the weld area was automatically extracted from the original X-ray films (3000+×800 + pixels) by the Gaussian positioning algorithm. Then, a multi-image hybrid stitching technology was proposed to reconstruct the long weld into a standard size of 1500 × 1500, which not only expanded the WSCR data set, but also effectively improved the data imbalance problem. In terms of model architecture, by improving U2Netp and UNet networks, MC-SPP module (multi-connection spatial pyramid pooling), RMAG module (residual multi-add gating recurrent unit), HDC-CBAM module (hybrid dilated convolution-convolutional block attention) and CCM module (cross-layer connection fusion) were integrated to form a cascade network with multi-level feature fusion capability. The experimental results showed that the model could effectively segment the defects such as cracks, pores, slag inclusion, incomplete fusion and incomplete penetration in the weld film, and the mIOU value reached 78.1, which was significantly superior to the existing methods, and provided an efficient and reliable solution for welding defect detection of pressure vessels, which had important engineering application value.https://doi.org/10.1038/s41598-025-02421-0Defect detectionSemantic segmentationWelding seam extractionData enhancement |
| spellingShingle | Xiaoyan Li Yi Wei Zhigang Lv Peng Wang Liangliang Li Mengyu Sun Chu Wang High resolution weld semantic defect detection algorithm based on integrated double U structure Scientific Reports Defect detection Semantic segmentation Welding seam extraction Data enhancement |
| title | High resolution weld semantic defect detection algorithm based on integrated double U structure |
| title_full | High resolution weld semantic defect detection algorithm based on integrated double U structure |
| title_fullStr | High resolution weld semantic defect detection algorithm based on integrated double U structure |
| title_full_unstemmed | High resolution weld semantic defect detection algorithm based on integrated double U structure |
| title_short | High resolution weld semantic defect detection algorithm based on integrated double U structure |
| title_sort | high resolution weld semantic defect detection algorithm based on integrated double u structure |
| topic | Defect detection Semantic segmentation Welding seam extraction Data enhancement |
| url | https://doi.org/10.1038/s41598-025-02421-0 |
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