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Automated Welding Defect Detection using Point-Rend ResUNet

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Abstract

In the field of welding inspection, radiographic non-destructive evaluation (NDE) is a widely used technique for detecting defects in welds. However, this technique requires professionally qualified workers to manually judge radiographs to determine the presence, type, and location of defects. Recently, deep learning techniques have been developed to automate this process by using image segmentation. Despite its effectiveness, small-size targets in segmentation can have blurred boundaries, making it difficult to accurately annotate them at the pixel level. In this study, we propose an automated approach using the Point-REND Res-UNet model to improve the accuracy of detecting welding defects. Our method uses the improved Point-Rend algorithm to iteratively refine coarse segmentation results, allowing for more accurate defect detection. We evaluate our approach on a set of X-ray data and demonstrate that it achieves an improvement in model dice of 6.22%. Our proposed approach can potentially save labor time and costs while enhancing the accuracy and efficiency of welding defect detection.

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The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The work is sponsored by the Beijing Institute of Technology Young Scholar Startup Program. The authors would like to acknowledge support from the Experimental Center of Advanced Materials (ECAM) of Beijing Institute of Technology and the National Key Laboratory of Science and Technology on Materials under Shock and Impact.

Funding

This work was supported by the Beijing Institute of Technology Young Scholar Startup Program.

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Authors and Affiliations

  1. School of Materials Science&Engineering, Beijing Institute of Technology, 100081, Beijing, People’s Republic of China

    Baoxin Zhang, Xiaopeng Wang, Jinhan Cui & Xinghua Yu

  2. Chongqing Innovation Center, Beijing Institute of Technology, 401147, Chongqing, People’s Republic of China

    Xinghua Yu

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  1. Baoxin Zhang
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  2. Xiaopeng Wang
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  3. Jinhan Cui
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  4. Xinghua Yu
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Contributions

BZ: conceptualization, methodology, software, original draft preparation. XW: data curation. JC: visualization, investigation. XY: supervision, writing—review and editing, project administration.

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Correspondence to Xinghua Yu.

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Zhang, B., Wang, X., Cui, J. et al. Automated Welding Defect Detection using Point-Rend ResUNet. J Nondestruct Eval 43, 11 (2024). https://doi.org/10.1007/s10921-023-01019-8

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  • DOI: https://doi.org/10.1007/s10921-023-01019-8

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