Residual Swin transformer-based weld crack leakage monitoring of pressure pipeline

Huang, Jing; Zhang, Zhifen; Qin, Rui; Yu, Yanlong; Li, Yongjie; Wen, Guangrui; Cheng, Wei; Chen, Xuefeng

doi:10.1007/s40194-023-01632-1

Weld World

2023

DOI: 10.1007/s40194-023-01632-1

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Residual Swin transformer-based weld crack leakage monitoring of pressure pipeline

Jing Huang,

Zhifen Zhang,

Rui Qin

et al.

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2024

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Cited by 4 publications

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WeldNet: a lightweight deep learning model for welding defect recognition

Wang,

et al. 2024

Weld World

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Weld defect detection is an important task in the welding process. Although there are many excellent weld defect detection models, there is still much room for improvement in stability and accuracy. In this study, a lightweight deep learning model called Weld-Net is proposed to improve the existing weld defect recognition network for its poor generalization performance, overfitting and large memory occupation, using a design with a small number of parameters but with better performance. We also proposed ensembledistillation strategy in the training process, which effectively improved the accuracy rate and proposed an improved model ensemble scheme. The experimental results show that the final designed WeldNet model performs well in detecting weld defects and achieves state-of-the-art performance. Its number of parameters is only 26.8% of that of ResNet18, but the accuracy is 8.9% higher, while achieving 41 FPS on cpu to meet the demand of real-time operation. The study is of guiding significance for solving practical problems in weld defect detection, and provides new ideas for the application of deep learning in industry. The code used in this article is available at: https://github.com/Wanglaoban3/WeldNet.git

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WeldNet: a lightweight deep learning model for welding defect recognition

Wang,

et al. 2024

Weld World

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Accelerable adaptive cepstrum and L2-Dual Net for acoustic emission-based quality monitoring in laser shock peening

Qin,

Zhang,

Huang

et al. 2024

Journal of Manufacturing Systems

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A review of underground transport infrastructure monitoring in CCS: Technology and Engineering Practice

Peng,

Xu,

et al. 2024

Process Safety and Environmental Protection

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Residual Swin transformer-based weld crack leakage monitoring of pressure pipeline

Cited by 4 publications

References 31 publications

WeldNet: a lightweight deep learning model for welding defect recognition

WeldNet: a lightweight deep learning model for welding defect recognition

Accelerable adaptive cepstrum and L2-Dual Net for acoustic emission-based quality monitoring in laser shock peening

A review of underground transport infrastructure monitoring in CCS: Technology and Engineering Practice

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