Skip to main content
SpringerLink
Log in

Recognition of weld defects from X-ray images based on improved convolutional neural network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

When convolutional neural network (CNN) is used for welding defect detection image recognition, the recognition result will be affected by many factors such as human factors, the activation function is sensitive to input parameters, and the edge features are weakened. In order to overcome the above problems, the methods include image processing, exponential linear unit (ELU) activation function and improved pooling model are used. According to the experiment, the image processing method can effectively segment the weld and defects, and the defect location in the weld image can be located. Using the ELU activation function in the CNN model can improve the robustness of the neural network to the input parameters and increase the sparsity of the network to increase the model’s convergence speed. The improved pooling method based on grayscale adaptation can increase the extraction range of weld defect features and reduce the impact of noise, and has certain dynamic adaptability to the defect features. The result shows that the improved convolutional neural network(ICNN) method can effectively improve the accuracy of recognition in weld image recognition, and the overall recognition rate can reach 98.13%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing [J]. Multimed Tools Appl 2:4–37

    Google Scholar 

  2. Bharodiya AK, Gonsai AM (2019) An improved edge detection algorithm for X-Ray images based on the statistical range. Heliyon 5:27–36

    Article  Google Scholar 

  3. Ding F, An-de H, Jian-kang H et al (2020) X-ray image defect recognition method for pipe weld based on improved convolutional neural network [J]. Transactions of the China Weld Institution 41(01):7–11 +97

    Google Scholar 

  4. Federer C, Xu H, Fyshe A et al (2020) Improved object recognition using neural networks trained to mimic the brain's statistical properties - ScienceDirect [J]. Neural Netw 131:103–114

    Article  Google Scholar 

  5. Gao L, Liao YL, Zhu BH et al (2007) Adaptive thresholds edge detection for image based on segmentation. Journal of Transport Information and Safety 25(5):73–76

    Google Scholar 

  6. Gorban AN, Mirkes EM, Tukin IY (2020) How deep should be the depth of convolutional neural networks: a backyard dog case study [J]. Cogn Comput 12(1):388–397

    Article  Google Scholar 

  7. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Comput Sci 77:354–377

    Google Scholar 

  8. He K, Zhang X, Ren S et al (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence 37(9):1904–1920

    Article  Google Scholar 

  9. Lecun Y, Bengio Y (2015) Hinton G. Deep Learning 521(7553):436–445

    Google Scholar 

  10. LeCun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  11. Li T, Meng Z, Ni B, Shen J, Wang M (2016) Robust geometric ℓp-norm feature pooling for image classification and action recognition. Image Vis Comput 55(2):64–76

    Article  Google Scholar 

  12. Li Y, Xie W, Li H (2017) Hyperspectral image reconstruction by deep convolutional neural network for classification. Pattern Recogn 63:371–383

    Article  Google Scholar 

  13. Lin JY, Ke HR, Chi BC et al (2007) Designing a classifier by a layered multi-population genetic programming approach. Pattern Recogn 40(8):2211–2225

    Article  Google Scholar 

  14. McIlhagga W (2010) The canny edge detector revisited. Int J Comput Vis 91(3):251–261

    Article  MathSciNet  Google Scholar 

  15. Mery D, Riffo V, Zscherpel U, Mondragón G, Lillo I, Zuccar I, Lobel H, Carrasco M (2015) GDXray: the database of X-ray images for nondestructive testing. J Nondestruct Eval 34(4):1–12

    Article  Google Scholar 

  16. O'Shea TJ, Hoydis J (2017) An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications & Networking 3(4):563–575

    Article  Google Scholar 

  17. Sakamoto M, Hiasa Y, Otake Y, Takao M, Suzuki Y, Sugano N, Sato Y (2020) Bayesian segmentation of hip and thigh muscles in metal artifact-contaminated CT using convolutional neural network-enhanced normalized metal artifact reduction [J]. Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology 92(3):335–344

    Article  Google Scholar 

  18. SHAO W, LIU X, WU Z. (2019) A robust weld seam detection method based on particle filter for laser welding by using a passive vision sensor [J]. Int J Adv Manuf Technol 4:1–10

    Google Scholar 

  19. Sun Y, Song HH, Bai P et al (2004) Real-time automatic detection of weld defects in X-ray images. Transactions of the China Welding Institution 25(2):115–118

    Google Scholar 

  20. Wang F, Zhu JJ (2017) Research of machine vision for welding defect in complex environment. Welding Technology 46(5):127–133

    Google Scholar 

  21. Wei Y, Xia W, Lin M et al (2015) HCP: a flexible CNN framework for multi-label image classification. IEEE Trans Softw Eng 38(9):1901–1907

    Google Scholar 

  22. Wu X, He R, Sun ZN et al (2017) A light CNN for deep face representation with Noisy labels. IEEE Transactions on Information Forensics & Security 14(8):1–13

    Google Scholar 

  23. Yang Z, Kuang N, Fan L et al (2018) Review of image classification algorithms based on convolutional neural networks. J Signal Process 34(12):84–99

    Google Scholar 

  24. Yang M, Huang F, Lv X (2019) A feature learning approach for face recognition with robustness to noisy label based on top-N prediction. Neurocomputing 330(22):48–55

    Article  Google Scholar 

  25. Yang L, Chen W, Liu W, Zha B, Zhu L (2020) Random noise attenuation based on residual convolutional neural network in seismic datasets [J]. IEEE Access 8:30271–30286

    Article  Google Scholar 

  26. Yong-wei YU, Liu-qing DU, Zhe Y et al (2016) Dynamic detection of casting defects radiographic image based on deep learning feature. Transactions of the Chinese Society for Agricultural Machinery 47(7):407–412

    Google Scholar 

  27. Zhang ZT, Cheng DD, Wang JJ et al (2019) Quantum based subgraph convolutional neural networks. Pattern Recogn 88:38–49

    Article  Google Scholar 

  28. Zhang Z, Ding S, Jia W (2019) A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems. Eng Appl Artif Intell 85:254–268

    Article  Google Scholar 

  29. Zhang Z, Ding S, Sun Y (2020) A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task. Neurocomputing 410:185–201

    Article  Google Scholar 

  30. Zhou FY, Jin LP, Dong J (2017) Review of convolutional neural network. Chinese Journal of Computers 40(6):1229–1251

    MathSciNet  Google Scholar 

Download references

Acknowledgments

This work is funded by the National Natural Science Foundation of China (No. 01020607).

Author information

Authors and Affiliations

  1. State Key Laboratory of Advanced Processing and Recycling of Non-ferrous Metals, Lanzhou University of Technology, Lanzhou, 730050, China

    Ande Hu & Ding Fan

  2. School of Materials Science and Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Lijian Wu & Jiankang Huang

  3. Baoshan Iron and Steel Co., Ltd, Shanghai, 201900, China

    Zhenya Xu

Authors
  1. Ande Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lijian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiankang Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ding Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhenya Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jiankang Huang or Ding Fan.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, A., Wu, L., Huang, J. et al. Recognition of weld defects from X-ray images based on improved convolutional neural network. Multimed Tools Appl 81, 15085–15102 (2022). https://doi.org/10.1007/s11042-022-12546-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12546-3

Keywords

Search

Navigation