Detection and Classification of Surface Defects of Magnetic Tile based on SE-U-Net

doi:10.23940/ijpe.20.12.p7.19101920

Abstract

Abstract: Defects such as blowholes and cracks are inevitable in the manufacturing of the magnet tile, and online full detection is a necessary process. Image-based surface defect detection is of great significance for improving product quality and production efficiency. This paper introduces a pixel-level surface defect detection method based on a deep full convolutional network, which realizes the detection and classification of defects at the same time. Combining the U-Net architecture and the squeeze-excitation module, an SE-U-Net that adaptively fuses shallow local information and deep semantic information is constructed. With a small amount of additional computation, U-Net's accuracy in detecting small defects from the large background is improved. Data augmentation via data transfer reduces the imbalance between image background and defects and improves the learning speed of the model. The proposed method was compared with SegNet and U-Net and achieved more accurate defect detection, and the average pixel accuracy reached 0.97, which demonstrates the superiority of the improved SE-U-Net for magnetic tile surface defects.

Key words: magnetic tile, multi-defect detection, deep learning, semantic segmentation, convolutional networks

Xincheng Cao, Wanshan Liu, Bin Yao, Qixin Lan, Weifang Sun. Detection and Classification of Surface Defects of Magnetic Tile based on SE-U-Net [J]. Int J Performability Eng, 2020, 16(12): 1910-1920.

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References

1. Y. Huang, C. Qiu, Y. Guo, X. Wang,K. Yuan, “Surface Defect Saliency of Magnetic Tile,” inProceedings of Conference on Automation Science and Engineering, pp. 612-617, 2018
2. X. Liu, K. Xu, P. Zhou, D. Zhou,Y. Zhou, “Surface Defect Identification of Aluminium Strips with Non-Subsampled Shearlet Transform,” Optics and Lasers in Engineering, Vol. 127, pp. 105986, 2020
3. K. Liu, N. Luo, A. Li, Y. Tian, H. Sajid,H. Chen, “A New Self-Reference Image Decomposition Algorithm for Strip Steel Surface Defect Detection,”IEEE Transactions on Instrumentation and Measurement, pp. 1, 2019
4. D. Tsai and D. E. R. Molina, “Morphology-based Defect Detection in Machined Surfaces with Circular Tool-Mark Patterns,”Measurement, Vol. 134, pp. 209-217, 2019
5. D. Huang, S. Liao, A. I. Sunny,S. Yu, “A Novel Automatic Surface Scratch Defect Detection for Fluid-Conveying Tube of Coriolis Mass Flow-Meter based on 2D-Direction Filter,”Measurement, Vol. 126, pp. 332-341, 2018
6. R. Gong, M. Chu, Y. H. Yang,Y. Feng, “A Multi-Class Classifier based on Support Vector Hyper-Spheres for Steel Plate Surface Defects,”Chemometrics and Intelligent Laboratory Systems, Vol. 188, pp. 70-78, 2019
7. R. Shanmugamani, M. Sadique,B. Ramamoorthy, “Detection and Classification of Surface Defects of Gun Barrels using Computer Vision and Machine Learning,”Measurement, Vol. 60, pp. 222-230, 2015
8. L. Yu, Z. Wang,Z. Duan, “Detecting Gear Surface Defects using Background-Weakening Method and Convolutional Neural Network,”Journal of Sensors, Vol. 2019, pp. 1-13, 2019
9. G. Z. Fu, P. Z. Sun, W. B. Zhu, J. X. Yang, Y. L. Cao, M. Y. Yang, et al., “A Deep-Learning-based Approach for Fast and Robust Steel Surface Defects Classification,”Optics and Lasers in Engineering, Vol. 121, pp. 397-405, 2019
10. J. Sun, P. Wang, Y. Luo,W. Li, “Surface Defects Detection based on Adaptive Multiscale Image Collection and Convolutional Neural Networks,” IEEE Transactions on Instrumentation and Measurement, Vol. 68, No. 12, pp. 4787-4797, 2019
11. J. Lian, W. K. Jia, M. Zareapoor, Y. J. Zheng, R. Luo, D. K. Jain, et al., “Deep-Learning-based Small Surface Defect Detection via an Exaggerated Local Variation-based Generative Adversarial Network,” IEEE Transactions on Industrial Informatics, Vol. 16, No. 2, pp. 1343-1351, 2020
12. B. Hariharan, P. Arbelaez, R. Girshick,J. Malik, “Hypercolumns for Object Segmentation and Fine-Grained Localization,” arXiv:1411.5752v2, pp. 447-456, 2015
13. M. A. Islam, N. D. B.Bruce, and Y. Wang, “Dense Image Labeling using Deep Convolutional Neural Networks,” inProceedings of Canadian Conference on Computer and Robot Vision, pp. 16-23, 2016
14. M. A. Islam, M. Rochan, S. Naha, N. D. B. Bruce,Y. Wang, “Gated Feedback Refinement Network for Coarse-to-Fine Dense Semantic Image Labeling,” arXiv:1806.11266v1, 2018
15. J. Long, E. Shelhamer,T. Darrell, “Fully Convolutional Networks for Semantic Segmentation,” arXiv:1411.4038, pp. 3431-3440, 2015
16. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,”Computer Science, 2014
17. M. Ranzato, F. J. Huang, Y. Boureau,Y. Lecun, “Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition,” inProceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2007
18. V. Badrinarayanan, A. Kendall,R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 12, pp. 2481-2495, 2017
19. O. Ronneberger, P. Fischer,T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” inProceedings of International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 234-241, 2015
20. T. Elsken, J. H. Metzen,F. Hutter, “Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution,” inProceedings of International Conference on Learning Representations, 2019
21. H. Liu, K. Simonyan,Y. Yang, “DARTS: Differentiable Architecture Search,” arXiv:1806.09055v2, 2018
22. A. Miech, I. Laptev,J. Sivic, “Learnable Pooling with Context Gating for Video Classification,” arXiv:1706.06905, 2017
23. C. S. Cao, X. M. Liu, Y. Yang,Y. N. Yu, “Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks,” inProceedings of International Conference on Computer Vision, pp. 2956-2964, 2015
24. J. S. Chung, A. W. Senior, O. Vinyals,A. Zisserman, “Lip Reading Sentences in the Wild,” in Proceedings of2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3444-3453, 2017
25. F. Wang, M. Q. Jiang, C. Qian,S. Yang, “Residual Attention Network for Image Classification,” arXiv:1704.06904v1, pp. 6450-6458, 2017
26. J. Hu, L. Shen, S. Albanie, G. Sun,E. Wu, “Squeeze-and-Excitation Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 42, No. 8, pp. 2011-2023, 2020

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