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Feature fusion–based preprocessing for steel plate surface defect recognition

State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China

Special Issues: Learning based Computational Bioinformatics for Healthcare Big Data

To address the problem of steel strip surface defect detection, a feature fusion–based preprocessing strategy is proposed based on machine vision technology. This strategy can increase the feature dimension of the image, highlight the pixel features of the image, and improve the recognition accuracy of the convolutional neural network. This method is based on commonly used image feature extraction operators (e.g., Sobel, Laplace, Prewitt, Robert, and local binary pattern) to process the defect image data, extract the edges and texture features of the defect image, and fuse the grayscale image processed by the feature operator with the original grayscale image by using three channels. To consider also computational efficiency and reduce the number of calculation parameters, the three channels are converted into a single channel according to a certain weight ratio. With this strategy, the steel plate surface defect database of NEU is processed, and fusion schemes with different operator combinations and different weight ratios for conversion to the single channel are explored. The test results show that, under the same network framework and with the same computational cost, the fusion scheme of Sobel:image:Laplace and the single-channel conversion weight ratio of 0.2:0.6:0.2 can improve the recognition rate of a previously unprocessed image by 3% and can achieve a final accuracy rate of 99.77%, thereby demonstrating the effectiveness of the proposed strategy.
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1. D. He, K. Xu, P. Zhou, D. Zhou, Surface defect classification of steels with a new semi-supervised learning method, Opt. Lasers Eng., 117 (2019), 40-48.

2. R. Gong, M. Chu, Y. Yang, Y. Feng, A multi-class classifier based on support vector hyper-spheres for steel plate surface defects, Chemom. Intell. Lab. Syst., 188 (2019), 70-78.

3. B. Wu, J. Zhou, X. Ji, Y. Yin, X. Shen, Research on Approaches for Computer Aided Detection of Casting Defects in X-ray Images with Feature Engineering and Machine Learning, Procedia Manuf., 37 (2019), 394-401.

4. X. Wen, K. Song, M. Niu, Z. Dong, Y. Yan, A three-dimensional inspection system for high temperature steel product surface sample height using stereo vision and blue encoded patterns, Optik, 130 (2017), 131-148

5. M. Kuffer, K. Pfeffer, R. Sliuzas, I. Baud, Extraction of slum areas from VHR imagery using GLCM variance, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9 (2016), 1830-1840.

6. Y. Liu, S. Liu, Z. Wang, Multi-focus image fusion with dense SIFT, Inf. Fusion, 23 (2015), 139-155.

7. A. Zendehboudi, M. A. Baseer, R. Saidur, Application of support vector machine models for forecasting solar and wind energy resources: A review, J. Cleaner Prod., 199 (2018), 272-285

8. I. Rish, An empirical study of the naive Bayes classifier, IJCAI 2001 workshop on empirical methods in artificial intelligence, 2001.

9. S. Zhang, X. Li, M. Zong, X. Zhu, R. Wang, Efficient knn classification with different numbers of nearest neighbors, IEEE Trans. Neural Networks Learn. Syst., 29 (2018), 1774-1785.

10. G. Biau, E. Scornet, A random forest guided tour, Test, 25 (2016), 197-227.

11. C. He, H. Kang, T. Yao, X. Li, An effective classifier based on convolutional neural network and regularized extreme learning machine, Math. Biosci. Eng., 16 (2019), 8309-8321.

12. L. Wen, Y. Dong, L. Gao, A new ensemble residual convolutional neural network for remaining useful life estimation, Math. Biosci. Eng., 16 (2019), 862-880.

13. Z. Ning, Y. Feng, M. Collotta, X. Kong, X. Wang, L. Guo, et al., Deep Learning in Edge of Vehicles: Exploring Tri-relationship for Data Transmission, IEEE Trans. Ind. Inf., 15 (2019), 5737-5746.

14. F. Chen, M. R. Jahanshahi, NB-CNN: Deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion, IEEE Trans. Ind. Electron., 65 (2018), 4392-4400.

15. Y. He, K. Song, Q. Meng, Y. Yan, An end-to-end steel surface defect detection approach via fusing multiple hierarchical features, IEEE Trans. Instrum. Meas., 69 (2020), 1493-1504.

16. Y. Wang, H. Xia, X. Yuan, L. Li, B. Sun, Distributed defect recognition on steel surfaces using an improved random forest algorithm with optimal multi-feature-set fusion, Multimedia Tools Appl., 77 (2018), 16741-16770.

17. S. Gupta, S. G. Mazumdar, Sobel edge detection algorithm, Int. J. Comput. Sci. Manage. Res., 2 (2013), 1578-1583.

18. W. Zheng, K. Liu, Research on Edge Detection Algorithm in Digital Image Processing, 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering, 2017.

19. D. Adlakha, D. Adlakha, R. Tanwar, Analytical comparison between Sobel and Prewitt edge detection techniques, Int. J. Sci. Eng. Res., 7 (2016), 1482-1485.

20. P. Vit, Comparison of various edge detection technique, Int. J. Signal Process. Image Process. Pattern Recognit., 9 (2016), 143-158.

21. T. Ahonen, A. Hadid, M. Pietikainen, Face description with local binary patterns: Application to face recognition, IEEE Trans. Pattern Anal. Mach. Intell., 28 (2006), 2037-2041.

22. K. Song, Y. Yan, A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects, Appl. Surf. Sci., 285 (2013), 858-864.

23. K. Li, X. Wang, L. Ji, Application of Multi-Scale Feature Fusion and Deep Learning in Detection of Steel Strip Surface Defect, International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), 2019.

24. A. El-Sawy, H. EL-Bakry, M. Loey, CNN for handwritten arabic digits recognition based on LeNet-5, International conference on advanced intelligent systems and informatics (AISI), 2016.

25. A. Krizhevsky, I. Sutskever, G. E. Hinton, ImageNet classification with deep convolutional neural networks, Advances in neural information processing systems (NIPS), 2012.

26. A. Vedaldi, A. Zisserman, Vgg convolutional neural networks practical, Dep. Eng. Sci. Univ. Oxford, 2016 (2016), 66.

27. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going deeper with convolutions, Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2015.

28. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.

29. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017.

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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