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http://dx.doi.org/10.5407/jksv.2021.19.3.063

A rubber o-ring defect detection system using data augmentation based on the SinGAN and random forest algorithm  

Lee, Yong Eun (School of Mechanical Engineering, Pusan National University)
Lee, Han Sung (Intown Co., LTD)
Kim, Dae Won (Intown Co., LTD)
Kim, Kyung Chun (School of Mechanical Engineering, Pusan National University)
Publication Information
Journal of the Korean Society of Visualization / v.19, no.3, 2021 , pp. 63-68 More about this Journal
Abstract
In this study, data was augmentation through the SinGAN algorithm using small image data, and defects in rubber O-rings were detected using the random forest algorithm. Unlike the commonly used data augmentation image rotation method to solve the data imbalance problem, the data imbalance problem was solved by using the SinGAN algorithm. A study was conducted to distinguish between normal products and defective products of rubber o-ring by using the random forest algorithm. A total of 20,000 image date were divided into transit and testing datasets, and an accuracy result was obtained to distinguish 97.43% defects as a result of the test.
Keywords
SinGAN; Data augmentation; Random forest algorithm; Image defect detection;
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1 Y. Lecun, Y. Bengio, and G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.   DOI
2 W. Yang, F. Zhou, R. Zhu, K. Fukui, G. Wang, and J. H. Xue, "Deep learning for image super-resolution," Neurocomputing, vol. 398, no. 10, pp. 291-292, 2020.   DOI
3 T. M. Oshiro, P. S. Perez, and J. A. Baranauskas, "How many trees in a random forest?," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7376 LNAI, no. May, pp. 154-168, 2012.
4 M. Belgiu and L. Dragu, "Random forest in remote sensing: A review of applications and future directions," ISPRS J. Photogramm. Remote Sens., vol. 114, pp. 24-31, 2016.   DOI
5 A. Kamilaris and F. X. Prenafeta-Boldu, "Deep learning in agriculture: A survey," Comput. Electron. Agric., vol. 147, no. July 2017, pp. 70-90, 2018.   DOI
6 C. Shorten and T. M. Khoshgoftaar, "A survey on Image Data Augmentation for Deep Learning," J. Big Data, vol. 6, no. 1, 2019.
7 S. P. Mohanty, D. P. Hughes, and M. Salathe, "Using deep learning for image-based plant disease detection," Front. Plant Sci., vol. 7, no. September, pp. 1-10, 2016.   DOI
8 J. Wang and L. Perez, "The effectiveness of data augmentation in image classification using deep learning," arXiv, 2017.
9 G. Biau and E. Scornet, "A random forest guided tour," Test, vol. 25, no. 2, pp. 197-227, 2016.   DOI
10 T. R. Shaham, T. Dekel, and T. Michaeli, "SinGAN: Learning a generative model from a single natural image," Proc. IEEE Int. Conf. Comput. Vis., vol. 2019-Octob, pp. 4569-4579, 2019.
11 C. Strobl, A. L. Boulesteix, A. Zeileis, and T. Hothorn, "Bias in random forest variable importance measures: Illustrations, sources and a solution," BMC Bioinformatics, vol. 8, 2007.
12 T. Shi and S. Horvath, "Unsupervised learning with random forest predictors," J. Comput. Graph. Stat., vol. 15, no. 1, pp. 118-138, 2006.   DOI
13 J. S. Ham, Y. Chen, M. M. Crawford, and J. Ghosh, "Investigation of the random forest framework for classification of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 492-501, 2005.   DOI
14 Z. Meng, X. Guo, Z. Pan, D. Sun, and S. Liu, "Data Segmentation and Augmentation Methods Based on Raw Data Using Deep Neural Networks Approach for Rotating Machinery Fault Diagnosis," IEEE Access, vol. 7, pp. 79510-79522, 2019.   DOI
15 V. F. Rodriguez-Galiano, B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez, "An assessment of the effectiveness of a random forest classifier for land-cover classification," ISPRS J. Photogramm. Remote Sens., vol. 67, no. 1, pp. 93-104, 2012.   DOI