Skip to main content

Advertisement

Springer Nature Link
Log in

RETRACTED ARTICLE: Capacitance pin defect detection based on deep learning

  • Published:
Journal of Combinatorial Optimization Aims and scope Submit manuscript

This article was retracted on 27 March 2024

This article has been updated

Abstract

Mask R-CNN network based on deep learning algorithm is specifically optimized for the small visual defects of gears. After comparison, the ResNet-101 residual neural network is used as the image sharing feature for network extraction. Subsequently, the unreasonable convolution of the feature extraction process \({P}_{5}\) in the characteristic pyramid network is removed to improve the defect detection rate indicator. Finally, the sizes of the anchor and label frames are adjusted according to the dimensioning of the tiny objects in the capacitance sample, and an appropriate aspect ratio is set to achieve the effective training of the network in the candidate area. Experiments show that the optimized Mask R-CNN network can achieve a defect detection rate that is above 98%.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

Change history

References

  • Akram MW, Li G, Jin Y et al (2019) CNN based automatic detection of photovoltaic cell defects in electroluminescence images. Energy 189:116319

    Article  Google Scholar 

  • Jing JF, Ma H, Zhang HH (2019) Automatic fabric defect detection using a deep convolutional neural network. Color Technol 135(3):213–223

    Article  Google Scholar 

  • Kang Z, Yuan C, Yang Q (2013) The fabric defect detection technology based on wavelet transform and neural network convergence. In: 2013 IEEE International Conference on Information and Automation (ICIA). IEEE, pp 597–601

  • Krummenacher G, Ong CS, Koller S et al (2018) Wheel defect detection with machine learning. IEEE Trans Intell Transp Syst 19(4):1176–1187

    Article  Google Scholar 

  • Le VK, Chen Z, Wong YW et al (2020) A complete online-SVM pipeline for case-based reasoning system: a study on pipe defect detection system. Soft Comput 24(22):16917–16933

    Article  Google Scholar 

  • Li Y, Zhao W, Pan J (2017) Deformable patterned fabric defect detection with fisher criterion-based deep learning. IEEE Trans Autom Sci Eng 14(2):1256–1264

    Article  Google Scholar 

  • Liu Z, Liu X, Li C et al (2018) Fabric defect detection based on faster R-CNN. In: Ninth International Conference on Graphic and Image Processing (ICGIP 2017), vol 10615. International Society for Optics and Photonics, p 106150A

  • Luo Q, Gao B, Woo WL et al (2019) Temporal and spatial deep learning network for infrared thermal defect detection. NDT and E Int 108:102164

    Article  Google Scholar 

  • Nturambirwe JFI, Opara UL (2020) Machine learning applications to non-destructive defect detection in horticultural products. Biosyst Eng 189:60–83

    Article  Google Scholar 

  • Prappacher N, Bullmann M, Bohn G et al (2020) Defect detection on rolling element surface scans using neural image segmentation. Appl Sci 10(9):3290

    Article  Google Scholar 

  • Sassi P, Tripicchio P, Avizzano CA (2019) A smart monitoring system for automatic welding defect detection. IEEE Trans Ind Electron 66:9641–9650

    Article  Google Scholar 

  • Seker A, Yüksek AG (2017) Stacked autoencoder method for fabric defect detection. Cumhuriyet Sci J 38(2):342–342

    Article  Google Scholar 

  • Silvén O, Niskanen M, Kauppinen H (2003) Wood inspection with non-supervised clustering. Mach vis Appl 13(5–6):275–285

    Article  Google Scholar 

  • Sun J, Li C, Wu X et al (2019) An effective method of weld defect detection and classification based on machine vision. IEEE Trans Ind Inform 15:6322–6333

    Article  Google Scholar 

Download references

Funding

This study was supported by scientific research foundation of Nanjing Vocational University of Industry Technology (YK18-03-03) and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (20KJB410005).

Author information

Authors and Affiliations

  1. College of Aeronautical Engineering, Nanjing Vocational University of Industry Technology, 1 Yangshan North Road, Qixia Dist, Nanjing, 210046, People’s Republic of China

    Cheng Cheng, Jie Huang, Yahong Zhuang, Tao Tang & Longlong Liu

  2. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Qinhuai Dist, Mailbox 357, Nanjing, 210016, People’s Republic of China

    Ning Dai

Authors
  1. Cheng Cheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ning Dai
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jie Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yahong Zhuang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Tao Tang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Longlong Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Cheng Cheng.

Ethics declarations

Conflict of interest

There is no conflict of interest among the authors.

Informed consent

Not applicable.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Publisher's Note

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

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10878-024-01130-0

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, C., Dai, N., Huang, J. et al. RETRACTED ARTICLE: Capacitance pin defect detection based on deep learning. J Comb Optim 44, 3477–3494 (2022). https://doi.org/10.1007/s10878-022-00904-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-022-00904-8

Keywords