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Machine-learning based Hybrid Method for Surface Defect Detection and Categorization in PU Foam

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Shailendra Singh Kathait, Sakshi Mathur
10.5120/ijca2018917668

Shailendra Singh Kathait and Sakshi Mathur. Machine-learning based Hybrid Method for Surface Defect Detection and Categorization in PU Foam. International Journal of Computer Applications 181(25):1-6, November 2018. BibTeX

@article{10.5120/ijca2018917668,
	author = {Shailendra Singh Kathait and Sakshi Mathur},
	title = {Machine-learning based Hybrid Method for Surface Defect Detection and Categorization in PU Foam},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2018},
	volume = {181},
	number = {25},
	month = {Nov},
	year = {2018},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume181/number25/30089-2018917668},
	doi = {10.5120/ijca2018917668},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Foam making is an important industry, their main applications being foam mattresses. Hence, their production in the industries is subject to very strict safety checks to ensure their quality. There are many types of defects that can arise during their manufacturing process, like holes, cuts, a misconfiguration in the material and many more. These defects are reviewed manually which leads to an inadequate accuracy and many defects are not detected. This paper proposes a novel approach that identifies defects in the foam material and on the surface using a hybrid method. Both supervised and unsupervised approaches are used to categorize materials based on normal or defective, including the type of defect. Then the reliable model is chosen according to the precision rates of both the models.

References

  1. Morphological Processing, chapter 8, pages 197–234.Wiley- Blackwell, 2011.
  2. John Akindoyo, Md Dalour Hossen Beg, Suriati Ghazali, Muhammad Islam, Nitthiyah Jeyaratnam, and Yuvaraj Ar. Polyurethane types, synthesis and applications-a review. 6:114453–114482, 11 2016.
  3. Raman Arora, Amitabh Basu, Poorya Mianjy, and Anirbit Mukherjee. Understanding deep neural networks with rectified linear units. In International Conference on Learning Representations, 2018.
  4. Dunne Campbell, R. A. Dunne, and N. A. Campbell. On the pairing of the softmax activation and cross–entropy penalty functions and the derivation of the softmax activation function.
  5. Xu Chu, Ihab F. Ilyas, Sanjay Krishnan, and Jiannan Wang. Data cleaning: Overview and emerging challenges. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD ’16, pages 2201–2206, New York, NY, USA, 2016. ACM.
  6. Rafael C. Gonzalez and Richard E.Woods. Digital image processing. Prentice Hall, Upper Saddle River, N.J., 2008.
  7. Cyril Goutte and Eric Gaussier. A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In Proceedings of the 27th European Conference on Advances in Information Retrieval Research, ECIR’05, pages 345–359, Berlin, Heidelberg, 2005. Springer-Verlag.
  8. Gareth Halfacree and Eben Upton. Raspberry Pi User Guide. Wiley Publishing, 1st edition, 2012.
  9. Itseez. The OpenCV Reference Manual, 2.4.9.0 edition, April 2014.
  10. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 25, pages 1097–1105. Curran Associates, Inc., 2012.
  11. Thomas M. Mitchell. Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edition, 1997.
  12. Sebastian Nowozin and Christoph H. Lampert. Structured learning and prediction in computer vision. Found. Trends. Comput. Graph. Vis., 6(3–4):185–365, March 2011.
  13. Keiron O’Shea and Ryan Nash. An introduction to convolutional neural networks. 11 2015.
  14. Iker Pastor-Lopez, Igor Santos, Aitor Santamaria-Ibirika, Mikel Salazar, Jorge de La-PeA-Sordo, and Pablo Bringas. Machine-learning-based surface defect detection and categorisation in high-precision foundry, 07 2012.
  15. Luis Perez and Jason Wang. The effectiveness of data augmentation in image classification using deep learning. CoRR, abs/1712.04621, 2017.
  16. Laura Rducu, Cristina Cozma, A.E.B. Stroescu, Adelaida Avino, M.D. Tanasescu, D.G. Balan, and Cristian Jecan. Our experience in chronic wounds care with polyurethane foam. 69:585–586, 03 2018.
  17. R. Sathya. Comparison of supervised and unsupervised learning algorithms for pattern classification.
  18. Matthew D. Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. CoRR, abs/1311.2901, 2013.

Keywords

Polymerization, Data Augmentation, Computer vision, OpenCV, Image Processing, Machine learning, Deep Learning, Convolutional Neural Networks