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) |
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 |