A Generative Adversarial Network approach for automatic inspection in automotive assembly lines
Resumo
In manufacturing systems, quality of inspection is a critical issue. This can be conducted by humans, or by employing Computer Vision Systems (CVS) which are trained upon representative datasets of images to detect classes of defects that may occur. The construction of such datasets strongly limits the use of CVS methods, as the variety of defects has combinatorial nature. Alternatively, instead of recognizing defects, a system can be trained to detect non-defective standards, becoming appropriate for some application profiles. In flexible automotive manufacturing, for example, parts are assembled within a reduced set of correct combinations, while the amount of possible incorrect assembling is enormous. In this paper, we show how a CVS can be extended with a Deep Learning-based approach that exploits a Generative Adversarial Network (GAN) to detect non-defective production, eliminating the need for constructing defect image datasets. The proposal is tested over the assembly line of Renault, in Brazil. Results show that our method returns better accuracy in inspection, compared with the current CVS solution, besides generalizing better to different components inspection without having to modify the method.
Palavras-chave:
Automatic inspection, Deep learning, Generative Adversarial Networks, Automotive manufacturing
Referências
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G. Wang, W. Kang, Q. Wu, Z. Wang, and J. Gao, "Generative adversarial network (gan) based data augmentation for palmprint recognition," in 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1-7, IEEE, 2018.
Z. Zheng, L. Zheng, and Y. Yang, "Unlabeled samples generated by gan improve the person re-identification baseline in vitro," in IEEE international conference on computer vision, pp. 3754-3762, 2017.
X. Wang, Z. Man, M. You, and C. Shen, "Adversarial generation of training examples: applications to moving vehicle license plate recognition," arXiv preprint arXiv:1707.03124, 2017.
M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, "Synthetic data augmentation using gan for improved liver lesion classification," in 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp. 289-293, IEEE, 2018.
H.-C. Shin, N. A. Tenenholtz, J. K. Rogers, C. G. Schwarz, M. L. Senjem, J. L. Gunter, K. P. Andriole, and M. Michalski, "Medical image synthesis for data augmentation and anonymization using generative adversarial networks," in International workshop on simulation and synthesis in medical imaging, pp. 1-11, Springer, 2018.
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X. Jiang and Z. Ge, "Data augmentation classifier for imbalanced fault classification," IEEE Transactions on Automation Science and Engineering, vol. 18, no. 3, pp. 1206-1217, 2021.
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," Advances in neural information processing systems, vol. 27, 2014.
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B. Esmaeilian, S. Behdad, and B. Wang, "The evolution and future of manufacturing: A review," Journal of Manufacturing Systems, vol. 39, pp. 79-100, 2016.
A. C. Caputo, P. M. Pelagagge, and P. Salini, "Modeling errors in kitting processes for assembly lines feeding," IFAC-PapersOnLine, vol. 48, no. 3, pp. 338-344, 2015.
M. E. A. Boudella, E. Sahin, and Y. Dallery, "Kitting optimisation in just-in-time mixed-model assembly lines: assigning parts to pickers in a hybrid robot-operator kitting system," International Journal of Production Research, vol. 56, no. 16, pp. 5475-5494, 2018.
X. Feng, Y. Jiang, X. Yang, M. Du, and X. Li, "Computer vision algorithms and hardware implementations: A survey," Integration, vol. 69, pp. 309-320, 2019.
M. Quintana, J. Torres, and J. M. Menéndez, "A simplified computer vision system for road surface inspection and maintenance," IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 3, pp. 608-619, 2015.
L. Li, K. Ota, and M. Dong, "Deep learning for smart industry: Efficient manufacture inspection system with fog computing," IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4665-4673, 2018.
A. Luckow, M. Cook, N. Ashcraft, E. Weill, E. Djerekarov, and B. Vorster, "Deep learning in the automotive industry: Applications and tools," in 2016 IEEE International Conference on Big Data (Big Data), pp. 3759-3768, IEEE, 2016.
M. Mazzetto, L. F. Southier, M. Teixeira, and D. Casanova, "Automatic classification of multiple objects in automotive assembly line," in 24th IEEE International Conference on Emerging Technologies and Factory Automation, pp. 363-369, IEEE, 2019.
B. Huval, T. Wang, S. Tandon, J. Kiske, W. Song, J. Pazhayampallil, M. Andriluka, P. Rajpurkar, T. Migimatsu, R. Cheng-Yue, et al., "An empirical evaluation of deep learning on highway driving," arXiv preprint arXiv:1504.01716, 2015.
D. Pomerleau, "Rapidly adapting artificial neural networks for autonomous navigation," in Advances in neural information processing systems, pp. 429-435, 1991.
M. Mazzetto, M. Teixeira, Érick Oliveira Rodrigues, and D. Casanova, "Deep learning models for visual inspection on automotive assembling line," International Journal of Advanced Engineering Research and Science, vol. 7, no. 1, pp. 473-494, 2020.
F. Di Mattia, P. Galeone, M. De Simoni, and E. Ghelfi, "A survey on gans for anomaly detection," arXiv preprint arXiv:1906.11632, 2019.
D. Wang, R. Vinson, M. Holmes, G. Seibel, A. Bechar, S. Nof, and Y. Tao, "Early detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (or-ac-gan)," Scientific reports, vol. 9, no. 1, pp. 1-14, 2019.
D. Mukherjee, A. Guha, J. M. Solomon, Y. Sun, and M. Yurochkin, "Outlier-robust optimal transport," in 38th International Conference on Machine Learning (M. Meila and T. Zhang, eds.), vol. 139 of Proceedings of Machine Learning Research, pp. 7850-7860, PMLR, 18-24 Jul 2021.
F. A. Saiz, G. Alfaro, I. Barandiaran, and M. Graña, "Generative adversarial networks to improve the robustness of visual defect segmentation by semantic networks in manufacturing components," Applied Sciences, vol. 11, no. 14, 2021.
A. Kusiak, "Convolutional and generative adversarial neural networks in manufacturing," International Journal of Production Research, vol. 58, pp. 1-11, 09 2019.
A. Deshpande, A. Minai, and M. Kumar, "One-shot recognition of manufacturing defects in steel surfaces," Procedia Manufacturing, vol. 48, pp. 1064-1071, 01 2020.
R. S. Peres, M. Azevedo, S. O. Araújo, M. Guedes, F. Miranda, and J. Barata, "Generative adversarial networks for data augmentation in structural adhesive inspection," Applied Sciences, vol. 11, no. 7, 2021.
D. R. Martin, C. C. Fowlkes, and J. Malik, "Learning to detect natural image boundaries using local brightness, color, and texture cues," IEEE transactions on pattern analysis and machine intelligence, vol. 26, no. 5, pp. 530-549, 2004.
T. Malekzadeh, M. Abdollahzadeh, H. Nejati, and N.-M. Cheung, "Aircraft fuselage defect detection using deep neural networks," arXiv preprint arXiv:1712.09213, 2017.
Y.-J. Cha, W. Choi, and O. Büyüköztürk, "Deep learning-based crack damage detection using convolutional neural networks," Computer-Aided Civil and Infrastructure Engineering, vol. 32, no. 5, pp. 361-378, 2017.
D. Mery, D. Hahn, and N. Hitschfeld, "Simulation of defects in aluminium castings using cad models of flaws and real x-ray images," Insight-Non-Destructive Testing and Condition Monitoring, vol. 47, no. 10, pp. 618-624, 2005.
D. Mery and D. Filbert, "Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence," IEEE Transactions on Robotics and Automation, vol. 18, no. 6, pp. 890-901, 2002.
A. Van Oord, N. Kalchbrenner, and K. Kavukcuoglu, "Pixel recurrent neural networks," in International Conference on Machine Learning, pp. 1747-1756, PMLR, 2016.
G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural computation, vol. 18, no. 7, pp. 1527-1554, 2006.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017.
D. P. Kingma and M. Welling, "Auto-encoding variational bayes," arXiv preprint arXiv:1312.6114, 2013.
A. Radford, L. Metz, and S. Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks," arXiv preprint arXiv:1511.06434, 2015.
M. Arjovsky, S. Chintala, and L. Bottou, "Wasserstein generative adversarial networks," in International conference on machine learning, pp. 214-223, PMLR, 2017.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, "Unpaired image-toimage translation using cycle-consistent adversarial networks," in IEEE international conference on computer vision, pp. 2223-2232, 2017.
A. M. Deshpande, A. A. Minai, and M. Kumar, "One-shot recognition of manufacturing defects in steel surfaces," Procedia Manufacturing, vol. 48, pp. 1064-1071, 2020. 10
G. Wang, W. Kang, Q. Wu, Z. Wang, and J. Gao, "Generative adversarial network (gan) based data augmentation for palmprint recognition," in 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1-7, IEEE, 2018.
Z. Zheng, L. Zheng, and Y. Yang, "Unlabeled samples generated by gan improve the person re-identification baseline in vitro," in IEEE international conference on computer vision, pp. 3754-3762, 2017.
X. Wang, Z. Man, M. You, and C. Shen, "Adversarial generation of training examples: applications to moving vehicle license plate recognition," arXiv preprint arXiv:1707.03124, 2017.
M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, "Synthetic data augmentation using gan for improved liver lesion classification," in 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp. 289-293, IEEE, 2018.
H.-C. Shin, N. A. Tenenholtz, J. K. Rogers, C. G. Schwarz, M. L. Senjem, J. L. Gunter, K. P. Andriole, and M. Michalski, "Medical image synthesis for data augmentation and anonymization using generative adversarial networks," in International workshop on simulation and synthesis in medical imaging, pp. 1-11, Springer, 2018.
F. Carrara, G. Amato, L. Brombin, F. Falchi, and C. Gennaro, "Combining gans and autoencoders for efficient anomaly detection," in 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3939- 3946, IEEE, 2021.
H. Arnelid, "Sensor modelling with recurrent conditional gans," Master's thesis, Chalmers University of Technology, 2018.
E. L. Zec, H. Arnelid, and N. Mohammadiha, "Recurrent conditional gans for time series sensor modelling," in Time Series Workshop at International Conference on Machine Learning,(Long Beach, California), 2019.
A. A. F. Saldivar, Y. Li, W.-n. Chen, Z.-h. Zhan, J. Zhang, and L. Y. Chen, "Industry 4.0 with cyber-physical integration: A design and manufacture perspective," in 21st international conference on Automation and computing, pp. 1-6, IEEE, 2015.
H. M. Do, K. C. Welch, and W. Sheng, "Soham: A sound-based human activity monitoring framework for home service robots," IEEE Transactions on Automation Science and Engineering, pp. 1-15, 2021.
M. Teixeira, J. E. Cury, and M. H. de Queiroz, "Exploiting distinguishers in local modular control of discrete-event systems," IEEE Transactions on Automation Science and Engineering, vol. 15, no. 3, pp. 1431-1437, 2018.
A. Pacana and K. Czerwińska, "Improving the quality level in the automotive industry," Production Engineering Archives, vol. 26, 2020.
K. Czerwińska, D. Siwiec, A. Pacana, and D. Malindžák, "Analysis of non-compliance of industrial robot arm parts," Zeszyty Naukowe. Organizacja i Zarzadzanie/Politechnika Ślaska, 2019.
A. Genaidy, A. Al-Shedi, and R. Shell, "Ergonomic risk assessment: preliminary guidelines for analysis of repetition, force and posture," Journal of human ergology, vol. 22, no. 1, pp. 45-55, 1993.
R. Szeliski, Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
KEYENCE, "Keyence." https://www.keyence.com.br/, March 2020.
WENGLOR, "Wenglor." https://www.wenglor.com/, March 2020.
COGNEX, "Cognex." https://www.cognex.com/, March 2020.
I. D. Apostolopoulos and M. Tzani, "Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. the case of multilevel vgg19," arXiv preprint arXiv:2011.11305, 2020.
J. Han, D. Zhang, G. Cheng, N. Liu, and D. Xu, "Advanced deeplearning techniques for salient and category-specific object detection: a survey," IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 84-100, 2018.
S. Niu, B. Li, X. Wang, and H. Lin, "Defect image sample generation with gan for improving defect recognition," IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, pp. 1611-1622, 2020.
X. Jiang and Z. Ge, "Data augmentation classifier for imbalanced fault classification," IEEE Transactions on Automation Science and Engineering, vol. 18, no. 3, pp. 1206-1217, 2021.
G. Owen, Game theory. Emerald Group Publishing, 2013.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," Advances in neural information processing systems, vol. 27, 2014.
D. M. Kreps, "Nash equilibrium," in Game Theory, pp. 167-177, Springer, 1989.
R. A. Yeh, C. Chen, T. Yian Lim, A. G. Schwing, M. Hasegawa-Johnson, and M. N. Do, "Semantic image inpainting with deep generative models," in IEEE conference on computer vision and pattern recognition, pp. 5485-5493, 2017.
I. J. Goodfellow, "On distinguishability criteria for estimating generative models," arXiv preprint arXiv:1412.6515, 2014.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, "Improved techniques for training gans," Advances in neural information processing systems, vol. 29, pp. 2234-2242, 2016.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, "Improved training of wasserstein gans," in Advances in Neural Information Processing Systems (I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, eds.), vol. 30, Curran Associates, Inc., 2017.
H. Petzka, A. Fischer, and D. Lukovnicov, "On the regularization of wasserstein gans," arXiv preprint arXiv:1709.08894, 2017.
A. Berg, J. Ahlberg, and M. Felsberg, "Unsupervised learning of anomaly detection from contaminated image data using simultaneous encoder training," arXiv preprint arXiv:1905.11034, 2019.
Publicado
24/10/2022
Como Citar
MUMBELLI, Joceleide D. C.; GUARNERI, Giovanni A.; LOPES, Yuri K.; CASANOVA, Dalcimar; TEIXEIRA, Marcelo.
A Generative Adversarial Network approach for automatic inspection in automotive assembly lines. In: WORKSHOP DE TESES E DISSERTAÇÕES - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 35. , 2022, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 62-71.
DOI: https://doi.org/10.5753/sibgrapi.est.2022.23262.
