SpringerLink
Log in

An Improved Wavelet Filtering Method for Welding Seam Images in a Complex Environment

  • Conference paper
  • First Online:
Advances in Mechanical Design (ICMD 2023)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 155))

Included in the following conference series:

  • 130 Accesses

Abstract

The quality of welding seam images is affected by noise during the process of acquisition and propagation. The traditional image-filtering method results in the loss of the edge and detail information of the welding seam image in the denoising process. In view of these problems, in this paper, we proposed an improved wavelet filtering method for welding seam images in complex environments. Based on wavelet filtering, the proposed method processes the high-frequency and low-frequency subbands of the image. The improved filtering method can better remove the noise of welding seem images. By using a wall-climbing robot equipped with an industrial camera to capture the weld image, various traditional filtering methods and the proposed filtering method were used to denoise the welding seam image. A comparison and analysis of the experimental results revealed that the proposed an improved wavelet filtering method for weld images in complex environments is better than the traditional filtering method. This study provides guidance for accurate weld identification and defect detection.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 379.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu, Z.F., Li, R.S., Yuan, J.T.: Research on weld surface notch monitoring based on microstrip antenna sensor array. Meas. Sci. Technol. 31(3), 1–10 (2019)

    Google Scholar 

  2. Tong, T.F., Yan, C.S., Sun, D.T.: Defects detection of weld image based on mathematical morphology and thresholding segmentation. In: 9th International Conference on Wireless Communications. Shanghai, China (2012)

    Google Scholar 

  3. Luo, Y.F., Jiang, W.C.S., Yang, Z.W.T.: Using reinforce plate to control the residual stresses and deformation during local postwelding heat treatment for ultra-large pressure vessels. Int. J. Press. Vessels Pip. 191 (2021)

    Google Scholar 

  4. Grewenig, S.F., Zimmer, S.S., Weickert, J.T.: Rotationally invariant similarity measures for nonlocal image denoising. J. Vis. Commun. Image Represent. 22(2), 117–130 (2011)

    Google Scholar 

  5. Mairal, J.F., Batch, F.S., Ponce, J.T.: Non-local sparse models for image restoration. In: International Conference on Computer Vision (2010)

    Google Scholar 

  6. Zhang, Q.F., Shen, X.S., Xu, L.T.: Rolling guidance filter. In: European Conference on Computer Vision, pp. 815–830 (2014)

    Google Scholar 

  7. Karacan, L.F., Erdem, E.S., Erdem, A.T.: Structure-preserving image smoothing via region covariances. ACM Trans. Graph. 32(6), 1–11 (2013)

    Google Scholar 

  8. Liu, W.F., Zhang, P.S., Huang, X.T.: Real-time image smoothing via iterative least squares. ACM Trans. Graph. 39(3), 1–24 (2020)

    Google Scholar 

  9. Yin, H.F., Gong, Y.S., Qiu, G.T.: Side window guided filtering. Signal Process. 165, 315–330 (2019)

    Google Scholar 

  10. Kumar, C.F., Prakash, R.S.: Ultrasound medical image de-noising using threshold based wavelet transformation method. J. Med. Imaging Health Inf. 10(8), 1825–1830 (2020)

    Google Scholar 

  11. Sumathi, K.F., Bindu, C.H.S.: Image denoising in wavelet domain with filtering and thresholding. Int. J. Eng. Technol. 7(3), 327–330 (2018)

    Google Scholar 

  12. Li, N.F., Zhang, J.S., Deng, Z.T.: Optimization of wavelet threshold denoising based on edge detection. In: Ninth International Conference on Digital Image Processing. Hong Kong, China (2017)

    Google Scholar 

  13. Zhang, L.F., Zhang, Y.S., Dai, B.T.: Welding defect detection based on local image enhancement. IET Image Process. 13(13), 2647–2658 (2019)

    Google Scholar 

  14. Liu, S.F.F., Pan, J.S.S., Yang M.S.T.: Learning Recursive Filters for Low-Level Vision Via a Hybrid Neural Network. Springer International Publishing, pp. 560–576 (2016)

    Google Scholar 

  15. Li, X.F., Ren, J.S., Yan, Q.T.: Deep edge-aware filters. In: International Conference on Machine Learning. The Chinese University of Hong Kong (2015)

    Google Scholar 

  16. Chen, Q.F., Jia, X.S., Koltun, V.T.: Fast image processing with fully-convolutional networks. Comput. Vis. Pattern Recogn. (2017)

    Google Scholar 

  17. Aghoutane, M.F.: Applying biorthogonal and orthogonal wavelets basis functions to the method of moments for modeling the helix antenna. J. Electromagnet. Waves Appl. 35(6), 822–832 (2021)

    Google Scholar 

  18. Pathak, D.F., Krahenbuhl, P.S., Donahue, J.T.: Context encoders: feature learning by inpainting. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2536–2544 (2016)

    Google Scholar 

Download references

Acknowledgements

This work is supported by the Science and Technology Plan of Inner Mongolia Autonomous Region of China (Grant No. 2021GG0260) and the Natural Science Foundation of Inner Mongolia Autonomous Region of China (Grant No. 2020LH06003). The authors are grateful for this support.

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, 010051, China

    Jie Deng & Wenzhi Zhang

  2. Inner Mongolia Key Laboratory of Special Service Intelligent Robotics, Hohhot, 010051, China

    Jie Deng & Wenzhi Zhang

Authors
  1. Jie Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenzhi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenzhi Zhang .

Editor information

Editors and Affiliations

  1. School of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China

    Jianrong Tan

  2. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Yu Liu

  3. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Hong-Zhong Huang

  4. School of Mechanical Engineering and Automation, Beihang University, Bei**g, China

    **gjun Yu

  5. University of Electronic Science and Technology of Chin, Chengdu, Sichuan, China

    Zequn Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Deng, J., Zhang, W. (2024). An Improved Wavelet Filtering Method for Welding Seam Images in a Complex Environment. In: Tan, J., Liu, Y., Huang, HZ., Yu, J., Wang, Z. (eds) Advances in Mechanical Design. ICMD 2023. Mechanisms and Machine Science, vol 155. Springer, Singapore. https://doi.org/10.1007/978-981-97-0922-9_89

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-0922-9_89

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0921-2

  • Online ISBN: 978-981-97-0922-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation