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.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

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

[1]	Janarthanan Sekar and Ganesh Kumar T. Hyperparameter Tuning in Deep Learning-Based Image Classification to Improve Accuracy using Adam Optimization [J]. Int J Performability Eng, 2023, 19(9): 579-586.
[2]	Sanjay Razdan, Himanshu Gupta, and Ashish Seth. D-SVM: A Deep Support Vector Machine Model with Different Kernel Types for Improved Intrusion Detection Performance [J]. Int J Performability Eng, 2023, 19(9): 598-606.
[3]	Aashita Rajput, Muskan Yadav, Sachin Yadav, Megha Chhabra, and Arun Prakash Agarwal. Patch-Based Breast Cancer Histopathological Image Classification using Deep Learning [J]. Int J Performability Eng, 2023, 19(9): 607-623.
[4]	Kavita Pandey, and Dhiraj Pandey. Real-Time Crop Disease Detection and Remedial Suggestion through Deep Learning-based Smartphone Application [J]. Int J Performability Eng, 2023, 19(8): 491-498.
[5]	Savita Khurana, Gaurav Sharma, and Bhawna Sharma. Hybrid Machine Learning Model for Load Prediction in Cloud Environment [J]. Int J Performability Eng, 2023, 19(8): 507-515.
[6]	Manvi Khatri and Ajay Sharma. Deep Learning Approach based on Iris, Face, and Palmprint Fusion for Multimodal Biometric Recognition System [J]. Int J Performability Eng, 2023, 19(6): 407-416.
[7]	Shreshtha Singh and Arun Sharma. State of the Art Convolutional Neural Networks [J]. Int J Performability Eng, 2023, 19(5): 342-349.
[8]	Vaishali Arya and Tapas Kumar. Boosting X-Ray Scans Feature for Enriched Diagnosis of Pediatric Pneumonia using Deep Learning Models [J]. Int J Performability Eng, 2023, 19(3): 175-183.
[9]	Shikha Choudhary and Bhawna Saxena. Image-Based Crop Disease Detection using Machine Learning Approaches: A Survey [J]. Int J Performability Eng, 2023, 19(2): 122-132.
[10]	Pavneet Singh, Jigyasa Chopra, Amandeep Singh, Nikita Nijhawan, and Kritika. Deep Learning Innovations for Enhanced Drusen Detection in Retinal Images [J]. Int J Performability Eng, 2023, 19(12): 779-787.
[11]	Mansi Mahendru and Sanjay Kumar Dubey. Portable Learning Approach towards Capturing Social Intimidating Activities using Big Data and Deep Learning Technologies [J]. Int J Performability Eng, 2022, 18(9): 668-678.
[12]	Sandhya Alagarsamy and Visumathi James. RNN LSTM-based Deep Hybrid Learning Model for Text Classification using Machine Learning Variant XGBoost [J]. Int J Performability Eng, 2022, 18(8): 545-551.
[13]	K. Lavanya, Smrithi Prakash, Yash Gedam, Altamash Aijaz, and L. Ramanathan. Real Time Digital Face Mask Detection using MobileNet-V2 and SSD with Apache Spark [J]. Int J Performability Eng, 2022, 18(8): 598-604.
[14]	Rajan Prasad Tripathi, Sunil Kumar Khatri, and Darelle Van Greunen. Relative Examination of Breast Malignant Growth Analysis Utilizing Different Machine Learning Algorithms [J]. Int J Performability Eng, 2022, 18(6): 417-425.
[15]	Poonam Narang, Ajay Vikram Singh, and Himanshu Monga. Hybrid Metaheuristic Approach for Detection of Fake News on Social Media [J]. Int J Performability Eng, 2022, 18(6): 434-443.