Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Automatic Metallic Surface Defect Detection using ShuffleDefectNet

  • Received : 2019.12.31
  • Accepted : 2020.02.24
  • Published : 2020.03.31
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Abstract

Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

일반적으로 품질 관리는 많은 제조 공정, 특히 주조 또는 용접과 관련된 공정의 기본 구성 요소가 된다. 그러나 사람이 일일이 수동으로 품질 관리 절차를 하는 것은 종종 시간이 걸리고 오류가 발생하기 쉽다. 최근 고품질 제품에 대한 요구를 만족시키기 위해 지능형 육안 검사 시스템의 사용이 생산 라인에서 필수적이 되고 있다. 본 논문에서는 이를 위해 딥 러닝 기반의 ShuffleDefectNet 결함 감지 시스템을 제안하고자 한다. 제안된 결함 검출 시스템은 NEU 데이터 세트의 결함 검출에 대한 여러 최신 성능들보다 높은 평균 정확도 99.75% 정도를 얻는다. 이 논문에서 여러 다른 트레이닝 데이터로부터 최상의 성능을 탐지하고 탐지 성능을 관찰하였다. 그 결과 ShuffleDefectNet의 전체 아키텍처를 사용할 때 정확성과 속도가 크게 향상됨을 알 수 있었다.

Keywords

References

  1. R. Rajkolhe and J. Khan, "Defects, causes and their remediesin casting process: A review," vol. 2, no. 3, pp. 375-383.
  2. X. Li, S. K. Tso, X.-P. Guan, and Q. Huang, "Improving automatic detection of defects in castings by applying wavelet technique," vol. 53, no. 6, pp. 1927-1934. https://doi.org/10.1109/TIE.2006.885448
  3. S. Ghorai, A. Mukherjee, M. Gangadaran, and P. K. Dutta, "Automatic defect detection on hot-rolled flat steel products," vol. 62, no. 3, pp. 612-621. https://doi.org/10.1109/TIM.2012.2218677
  4. LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553), 436. https://doi.org/10.1038/nature14539
  5. Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" (Submitted on 30 Jul 2018)
  6. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Inverted residuals and linear bottlenecks: Mobile networks for classification, detection, and segmentation. arXiv preprint arXiv:1801.04381 (2018)
  7. Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. arXiv preprint arXiv:1707.01083 (2017)
  8. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. arXiv preprint (2016)
  9. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for im-age classification. In: Proc. of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642-3649 (June 2012)
  10. Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning - a new frontier inartificial intelligence research. IEEE Computational Intelligence Magazine 5(4),13-18 (2010) https://doi.org/10.1109/MCI.2010.938364
  11. Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Proc. of International Conference on Document Analysis and Recognition (ICDAR), pp. 958-963 (2003)
  12. Coates, A., Lee, H., Ng, A.Y.: An analysis of single-layer networks in unsupervised feature learning. In: Proc. of International Conference on Artificial intelligence and statistics (AISTATS) (2011)
  13. Soukup, D., Huber-Mwork, R.: Convolutional neural networks for steel surface defect detection from photometric stereo images. Advances in Visual Computing 8887 (2014) 668-677 3
  14. Ke, W., Huiqin, W., Yue, S., Li, M., Fengyan, Q.: Banknote Image Defect Recognition Method Based on Convolution Neural Network. International Journal of Security and Its Applications 10(6) (2016) 269-280 3
  15. Faghih-Roohi, S., Hajizadeh, S., Nu-nez, A., Babuska, R., Schutter, B.D.: Deep convolutional neural networks for detection of rail surface defects. In: 2016 International Joint Conference on Neural Networks (IJCNN). (2016) 2584-2589 3
  16. Park, J.K., Kwon, B.K., Park, J.H., Kang, D.J.: Machine learning-based imaging system for surface defect inspection. International Journal of Precision Engineering and Manufacturing-Green Technology 3(3) (Jul 2016) 303-310 3 https://doi.org/10.1007/s40684-016-0039-x
  17. Weimer, D., Scholz-Reiter, B., Shpitalni, M.: Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Annals - Manufacturing Technology 65(1) (Jan 2016) 417-420 4 https://doi.org/10.1016/j.cirp.2016.04.072
  18. K. Song and Y. Yan, "A noise-robust method based on completed local binary patterns for hot-rolled steel strip surface defects," Appl. Surface Sci., vol. 285, pp. 858-864, Nov. 2013. https://doi.org/10.1016/j.apsusc.2013.09.002
  19. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Inverted residuals and linear bottlenecks: Mobile networks for classification, detection, and segmentation. arXiv preprint arXiv:1801.04381 (2018)
  20. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012
  21. S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He. Aggregated residual transformations for deep neural networks. In Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on, pages 5987-5995. IEEE, 2017.
  22. Yu He, Kitchen Song, Qinggang Meng, Yunhui Yan. An End-to-end Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features. DOI 10.1109/TIM.2019.2915404, IEEE
  23. K. Song and Y. Yan, "A noise-robust method based on completed local binary patterns for hot-rolled steel strip surface defects," Appl. Surface Sci., vol. 285, pp. 858-864, Nov. 2013. https://doi.org/10.1016/j.apsusc.2013.09.002
  24. M. Xiao, M. Jiang, and G. Li et al., "An evolutionary classifier for steel surface defects with a small sample set," EURASIP J. Image Vid. Process., vol. 2017, no. 1, pp. 48-61, Dec. 2017 https://doi.org/10.1186/s13640-017-0197-y
  25. P. Chen, and S.S. Ho, "Is over feat useful for image-based surface defect classification tasks?" in Proc. IEEE Int. Conf. Image Process. (ICIP), AZ, USA, Sep. 2016, pp. 749-753.
  26. R. Ren, T. Hung, and K.C. Tan, "A generic deep-learning-based approach for automated surface inspection," IEEE Trans. Cybern., vol. 48, no. 3, pp. 929-940, Mar. 2018. https://doi.org/10.1109/tcyb.2017.2668395
  27. Y. Lecun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, May 2015. https://doi.org/10.1038/nature14539