Skip to main content
SpringerLink
Log in

Surface defect detection method for air rudder based on positive samples

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

In actual industrial applications, the defect detection performance of deep learning models mainly depends on the size and quality of training samples. However, defective samples are difficult to obtain, which greatly limits the application of deep learning-based surface defect detection methods to industrial manufacturing processes. Aiming at solving the problem of insufficient defective samples, a surface defect detection method based on Frequency shift-Convolutional Autoencoder (Fs-CAE) network and Statistical Process Control (SPC) thresholding was proposed. The Fs-CAE network was established by adding frequency shift operation on the basis of the CAE network. The loss of high-frequency information can be prevented through the Fs-CAE network, thereby lowering the interference to defect detection during image reconstruction. The incremental SPC thresholding was introduced to detect defects automatically. The proposed method only needs samples without defects for model training and does not require labels, thus reducing manual labeling time. The surface defect detection method was tested on the air rudder image sets from the image acquisition platform and data augmentation methods. The experimental results indicated that the detection performance of the method proposed in this paper was superior to other surface defect detection methods based on image reconstruction and object detection algorithms. The new method exhibits low false positive rate (FP rate, 0%), low false negative rate (FN rate, 10%), high accuracy (95.19%) and short detection time (0.35 s per image), which shows great potential in practical industrial applications.

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
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Abu, M., Amir, A., Lean, Y. H., Zahri, N. A. H., & Azemi, S. A. (2021). The performance analysis of transfer learning for steel defect detection by using deep learning. In Journal of Physics: Conference Series, 1755(1), 012041.

    Google Scholar 

  • Badmos, O., Kopp, A., Bernthaler, T., & Schneider, G. (2020). Image-based defect detection in lithium-ion battery electrode using convolutional neural networks. Journal of Intelligent Manufacturing, 31(4), 885–897.

    Article  Google Scholar 

  • Božič, J., Tabernik, D., & Skočaj, D. (2021). Mixed supervision for surface-defect detection: From weakly to fully supervised learning. Computers in Industry, 129, 103459.

    Article  Google Scholar 

  • Buckland, M., & Gey, F. (1994). The relationship between recall and precision. Journal of the American Society for Information Science, 45(1), 12–19.

    Article  Google Scholar 

  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4(1), 55–81.

    Article  Google Scholar 

  • Chow, J. K., Su, Z., Wu, J., Tan, P. S., Mao, X., & Wang, Y. H. (2020). Anomaly detection of defects on concrete structures with the convolutional autoencoder. Advanced Engineering Informatics, 45, 101105.

    Article  Google Scholar 

  • Gao, Y., Gao, L., Li, X., & Wang, X. V. (2019). A multilevel information fusion-based deep learning method for vision-based defect recognition. IEEE Transactions on Instrumentation and Measurement, 69(7), 3980–3991.

    Article  Google Scholar 

  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 1–9.

    Google Scholar 

  • He, J., Zheng, J., Shen, Y., Guo, Y., & Zhou, H. (2020). Facial image synthesis and super-resolution with stacked generative adversarial network. Neurocomputing, 402, 359–365.

    Article  Google Scholar 

  • Heger, J., Desai, G., & El Abdine, M. Z. (2020). Anomaly detection in formed sheet metals using convolutional autoencoders. Procedia CIRP, 93, 1281–1285.

    Article  Google Scholar 

  • Hoo, Z. H., Candlish, J., & Teare, D. (2017). What is an ROC curve? Emergency Medicine Journal, 34(6), 357–359.

    Article  Google Scholar 

  • Hu, G. H. (2015). Automated defect detection in textured surfaces using optimal elliptical Gabor filters. Optik, 126(14), 1331–1340.

    Article  Google Scholar 

  • Hu, H., Bai, S., Li, A., Cui, J., & Wang, L. (2021). Dense relation distillation with context-aware aggregation for few-shot object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10185–10194).

  • Jain, S., Seth, G., Paruthi, A., Soni, U., & Kumar, G. (2020). Synthetic data augmentation for surface defect detection and classification using deep learning. Journal of Intelligent Manufacturing, 33, 1007–1020.

    Article  Google Scholar 

  • Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.

  • Li, B., Yang, B., Liu, C., Liu, F., Ji, R., & Ye, Q. (2021a). Beyond max-margin: Class margin equilibrium for few-shot object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7363–7372).

  • Li, Y., Zhu, H., Cheng, Y., Wang, W., Teo, C, S., Xiang, C., Vadakkepat, P., & Lee, T. H. (2021b). Few-shot object detection via classification refinement and distractor retreatment. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 15395–15403).

  • Liu, J., Song, K., Feng, M., Yan, Y., Tu, Z., & Zhu, L. (2021a). Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection. Optics and Lasers in Engineering, 136, 106324.

    Article  Google Scholar 

  • Liu, L., Cao, D., Wu, Y., & Wei, T. (2019). Defective samples simulation through adversarial training for automatic surface inspection. Neurocomputing, 360, 230–245.

    Article  Google Scholar 

  • Liu, S., Bao, J., Lu, Y., Li, J., Lu, S., & Sun, X. (2021b). Digital twin modeling method based on biomimicry for machining aerospace components. Journal of Manufacturing Systems, 58, 180–195.

    Article  Google Scholar 

  • Liu, X., Tang, X., & Chen, S. (2021c). Learning a similarity metric discriminatively with application to ancient character recognition. In International Conference on Knowledge Science, Engineering and Management, 12815, 614–626.

    Google Scholar 

  • Lobo, J. M., Jiménez-Valverde, A., & Real, R. (2008). AUC: A misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17(2), 145–151.

    Article  Google Scholar 

  • Luo, T., Ma, Z., Wang, Z., Xu, Z. Q. J., & Zhang, Y. (2021). An upper limit of decaying rate with respect to frequency in deep neural network. arXiv preprint arXiv:2105.11675.

  • Mao, X., Shen, C., & Yang, Y. B. (2016). Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Advances in Neural Information Processing Systems, 29, 1–9.

    Google Scholar 

  • Mujeeb, A., Dai, W., Erdt, M., & Sourin, A. (2019). One class based feature learning approach for defect detection using deep autoencoders. Advanced Engineering Informatics, 42, 100933.

    Article  Google Scholar 

  • Norouzi, M., Fleet, D. J., & Salakhutdinov, R. R. (2012). Hamming distance metric learning. Advances in Neural Information Processing Systems, 25, 1061–1069.

    Google Scholar 

  • Qin, Z., Liu, Z., Zhu, P., & Xue, Y. (2020). A GAN-based image synthesis method for skin lesion classification. Computer Methods and Programs in Biomedicine, 195, 105568.

    Article  Google Scholar 

  • Qiu, P. (2020). Big data? Statistical process control can help! The American Statistician, 74(4), 329–344.

    Article  Google Scholar 

  • Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.

  • Ren, R., Hung, T., & Tan, K. C. (2017). A generic deep-learning-based approach for automated surface inspection. IEEE Transactions on Cybernetics, 48(3), 929–940.

    Article  Google Scholar 

  • Sanghadiya, F., & Mistry, D. (2015). Surface defect detection in a tile using digital image processing: Analysis and evaluation. International Journal of Computer Applications, 116(10), 33–35.

    Article  Google Scholar 

  • Singh, S. A., & Desai, K. A. (2022). Automated surface defect detection framework using machine vision and convolutional neural networks. Journal of Intelligent Manufacturing, 1–17.

  • Soukup, D., & Huber-Mörk, R. (2014). Convolutional neural networks for steel surface defect detection from photometric stereo images. In International Symposium on Visual Computing, 8887, 668–677.

    Google Scholar 

  • Sun, B., Li, B., Cai, S., Yuan, Y., & Zhang, C. (2021). Fsce: Few-shot object detection via contrastive proposal encoding. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 7352–7362).

  • Sun, Y., Yin, S., & Teng, L. (2020). Research on multi-robot intelligent fusion technology based on multi-mode deep learning. International Journal of Electronics and Information Engineering, 12(3), 119–127.

    Google Scholar 

  • Szarski, M., & Chauhan, S. (2022). An unsupervised defect detection model for a dry carbon fiber textile. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-01964-7

    Article  Google Scholar 

  • Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2020). Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing, 31(3), 759–776.

    Article  Google Scholar 

  • Tao, J., Qin, C., Xiao, D., Shi, H., Ling, X., Li, B., & Liu, C. (2020). Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method. Journal of Intelligent Manufacturing, 31(5), 1243–1255.

    Article  Google Scholar 

  • Tao, X., Zhang, D. P., Ma, W., Hou, Z., Lu, Z., & Adak, C. (2022). Unsupervised anomaly detection for surface defects with dual-siamese network. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2022.3142326

    Article  Google Scholar 

  • Torabi, A. R., Shams, S., Narab, M. F., & Atashgah, M. A. (2021). Unsteady aero-elastic analysis of a composite wing containing an edge crack. Aerospace Science and Technology, 115, 106769.

    Article  Google Scholar 

  • Tsai, D. M., & Jen, P. H. (2021). Autoencoder-based anomaly detection for surface defect inspection. Advanced Engineering Informatics, 48, 101272.

    Article  Google Scholar 

  • Tsai, D. M., & Luo, J. Y. (2010). Mean shift-based defect detection in multicrystalline solar wafer surfaces. IEEE Transactions on Industrial Informatics, 7(1), 125–135.

    Article  Google Scholar 

  • Tschannen, M., Bachem, O., & Lucic, M. (2018). Recent advances in autoencoder-based representation learning. arXiv preprint arXiv:1812.05069.

  • Wei, P., Liu, C., Liu, M., Gao, Y., & Liu, H. (2018). CNN-based reference comparison method for classifying bare PCB defects. The Journal of Engineering, 16, 1528–1533.

    Article  Google Scholar 

  • Wu, S., Wu, Y., Cao, D., & Zheng, C. (2019). A fast button surface defect detection method based on Siamese network with imbalanced samples. Multimedia Tools and Applications, 78(24), 34627–34648.

    Article  Google Scholar 

  • Xu, Z. Q. J., Zhang, Y., & Xiao, Y. (2019). Training behavior of deep neural network in frequency domain. In International Conference on Neural Information Processing, 11953, 264–274.

    Google Scholar 

  • Yi, L., Li, G., & Jiang, M. (2017). An end-to-end steel strip surface defects recognition system based on convolutional neural networks. Steel Research International, 88(2), 1600068.

    Article  Google Scholar 

  • Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? Advances in Neural Information Processing Systems, 27, 3320–3328.

    Google Scholar 

Download references

Acknowledgements

This work is partially supported by National Natural Science Foundation of China (Grant number 52175461, 11632004 and U1864208); Intelligent Manufacturing Project of Tianjin (Grant number 20201199); Fund for the High-level Talents Funding Project of Hebei Province (Grant number B2021003027); Key Program of Research and Development of Hebei Province (Grant number 202030507040009); Innovative Research Groups of Natural Science Foundation of Hebei Province (Grant number A2020202002); Top Young Talents Project of Hebei Province, China (Grant number 210014).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China

    Zeqing Yang, Mingxuan Zhang, Yingshu Chen, Ning Hu, Lingxiao Gao, Libing Liu & Enxu Ping

  2. Key Laboratory of Hebei Province on Scale-Span Intelligent Equipment Technology, Tianjin, 300130, China

    Zeqing Yang, Yingshu Chen, Ning Hu, Lingxiao Gao & Libing Liu

  3. State Key Laboratory of Reliability and Intelligence Electrical Equipment, Hebei University of Technology, Tianjin, 300130, China

    Ning Hu

  4. Department of Mechanical Engineering, Changwon National University, Changwon, 641773, Republic of Korea

    Jung Il Song

Authors
  1. Zeqing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingxuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingshu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ning Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lingxiao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Libing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Enxu Ping
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jung Il Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ning Hu.

Ethics declarations

Competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor 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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Z., Zhang, M., Chen, Y. et al. Surface defect detection method for air rudder based on positive samples. J Intell Manuf 35, 95–113 (2024). https://doi.org/10.1007/s10845-022-02034-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-022-02034-8

Keywords

Search

Navigation