Optimizing Generative Adversarial Network (GAN) Models for Non-Pneumatic Tire Design
Source abstract
Abstract
Pneumatic tires are used in diverse industries. However, their design is difficult, as it relies on the knowledge of experienced designers. In this paper, we generate images of non-pneumatic tire designs with patterns based on shapes and lines for different generative adversarial network (GAN) models and test the performance of the models. Using OpenCV, 2000 training images were generated, corresponding to spoke, curve, triangle, and honeycomb non-pneumatic tires. The images created for training were used after removing highly similar images by applying mean squared error (MSE) and structural similarity index (SSIM). To identify the best model for generating patterns of regularly shaped non-pneumatic tires, GAN, deep convolutional generative adversarial network (DCGAN), StarGAN v2, StyleGAN v2-ADA, and ProjectedGAN were compared and analyzed. In the qualitative evaluation, the GAN, DCGAN, StarGAN v2, and StyleGAN v2-ADA models distorted the circle shape and did not maintain a consistent pattern, but ProjectedGAN retained consistency in the circle, and the pattern was less distorted than in the other GAN models. When evaluating quantitative metrics, ProjectedGAN performed the best among several techniques when the difference between the generated and actual image distributions was measured.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Image Processing Techniques · Generative Adversarial Networks and Image Synthesis · Image Processing Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Division of Computer Science and Engineering, Sunmoon University, Asan 31460, Republic of Korea
Department of Artificial Intelligence and Software Technology, Sunmoon University, Asan 31460, Republic of Korea
Funding
Sun Moon University Research Grant
References (61)
Show source-provided references
1. Gent, A.N., and Walter, J.D. (2006). Pneumatic Tire, Mechanical Engineering Faculty Research.
2. Mykola, K., Prentkovskis, O., and Skačkauskas, P. (2022, January 20–21). Comparison Analysis Between Pneumatic and Airless Tires by Computational Modelling for Avoiding Road Traffic Accidents. Proceedings of the International Conference on Reliability and Statistics in Transportation and Communication, Riga, Latvia.
3. Introduction to Technology Trends, Problems and Solutions of Non-Pneumatic
4. Study of tweel non-pneumatic tires10.22214/ijraset.2022.39990 ↗
5. Non-pneumatic tire designs suitable for fused filament fabrication: An overview10.1016/j.prostr.2022.12.140 ↗
6. Generative adversarial networks10.1145/3422622 ↗
7. Karras, T., Laine, S., and Aila, T. (2019, January 15–20). A Style-Based Generator Architecture for Generative Adversarial Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00453 ↗
8. Gatys, L.A., Ecker, A.S., and Bethge, M. (2015). A neural algorithm of artistic style. arXiv.10.1167/16.12.326 ↗
9. Mao, H., Cheung, M., and She, J. (2017, January 23–27). Deepart: Learning joint representations of visual arts. Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA.10.1145/3123266.3123405 ↗
10. Donovan-Maiye, R.M., Brown, J.M., Chan, C.K., Ding, L., Yan, C., Gaudreault, N., Theriot, J.A., Maleckar, M.M., Knijnenburg, T.A., and Johnson, G.R. (2022). A deep generative model of 3D single-cell organization. PLoS Comput. Biol., 18.10.1371/journal.pcbi.1009155 ↗
11. Kim, S.W., Zhou, Y., Philion, J., Torralba, A., and Fidler, S. (2020, January 14–19). Learning to simulate dynamic environments with gamegan. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00131 ↗
12. Ratliff, L.J., Burden, S.A., and Sastry, S.S. (2013, January 2–4). Characterization and computation of local Nash equilibria in continuous games. Proceedings of the 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.10.1109/Allerton.2013.6736623 ↗
13. Goodfellow, I. (2016). Nips 2016 tutorial: Generative adversarial networks. arXiv.
14. Improved techniques for training gans
15. Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6–11). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia.
16. Differentiable augmentation for data-efficient gan training
17. Ulyanov, D., Vedaldi, A., and Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv.
18. Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv.
19. Choi, Y., Uh, Y., Yoo, J., and Ha, J.W. (2020, January 14–19). Stargan v2: Diverse image synthesis for multiple domains. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00821 ↗
20. Training generative adversarial networks with limited data
21. Projected gans converge faster
22. Gans trained by a two time-scale update rule converge to a local nash equilibrium
23. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18–23). The unreasonable effectiveness of deep features as a perceptual metric. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00068 ↗
24. Brock, A., Donahue, J., and Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv.
25. Development of a non-pneumatic wheel10.2346/1.2345642 ↗
26. A comprehensive review on non-pneumatic tyre research10.1016/j.matdes.2023.111742 ↗
27. Flexible cellular solid spokes of a non-pneumatic tire10.1016/j.compstruct.2011.12.022 ↗
28. Sim, J., Hong, J., Cho, I., and Lee, J. (2021). Analysis of vertical stiffness characteristics based on spoke shape of non-pneumatic tire. Appl. Sci., 11.10.3390/app11052369 ↗
29. Image quality assessment: From error visibility to structural similarity10.1109/TIP.2003.819861 ↗
30. Chemane, L., and Mapsanganhe, S. (2010, January 19–21). Distributed Government e-Mail Service: Mozambique GovNet Case Study. Proceedings of the 2010 IST-Africa, Durban, South Africa.
31. O’Shea, K., and Nash, R. (2015). An introduction to convolutional neural networks. arXiv.
32. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
33. Springenberg, J.T., Dosovitskiy, A., Brox, T., and Riedmiller, M. (2014). Striving for simplicity: The all convolutional net. arXiv.
34. Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv.
35. Ioffe, S., and Szegedy, C. (2015, January 6–11). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France.
36. Nair, V., and Hinton, G.E. (2010, January 21–24). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel.
37. Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 16–21). Rectifier nonlinearities improve neural network acoustic models. Proceedings of the the 30th International Conference on Machine Learning, Atlanta, GA, USA.
38. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
39. Bengio, S., Bengio, Y., Cloutier, J., and Gecsei, J. (2013). Optimality in Biological and Artificial Networks?, Routledge.
40. Gholamalinezhad, H., and Khosravi, H. (2020). Pooling methods in deep neural networks, a review. arXiv.
41. Nwankpa, C., Ijomah, W., Gachagan, A., and Marshall, S. (2018). Activation functions: Comparison of trends in practice and research for deep learning. arXiv.
42. (2023, June 02). DCGAN TUTORIAL. Available online: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html.
43. Huang, X., and Belongie, S. (2017, January 22–29). Arbitrary style transfer in real-time with adaptive instance normalization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.167 ↗
44. (2023, June 02). Stargan-v2. Available online: https://github.com/clovaai/stargan-v2.
45. Dropout: A simple way to prevent neural networks from overfitting
46. Improved Consistency Regularization for GANs
47. Bora, A., Price, E., and Dimakis, A.G. (May, January 30). AmbientGAN: Generative models from lossy measurements. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada.
48. Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (2018). Spectral normalization for generative adversarial networks. arXiv.
49. Tan, M., and Le, Q. (2019, January 9–15). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA.
50. Imagenet classification with deep convolutional neural networks10.1145/3065386 ↗
51. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
52. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv.
53. Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and Jégou, H. (2021, January 18–24). Training data-efficient image transformers & distillation through attention. Proceedings of the International Conference on Machine Learning, Virtual.
54. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7–12). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
55. Shlens, J. (2014). Notes on kullback-leibler divergence and likelihood. arXiv.
56. Korhonen, J., and You, J. (2012, January 5–7). Peak signal-to-noise ratio revisited: Is simple beautiful?. Proceedings of the 2012 Fourth International Workshop on Quality of Multimedia Experience, Melbourne, Australia.10.1109/QoMEX.2012.6263880 ↗
57. FSIM: A feature similarity index for image quality assessment10.1109/TIP.2011.2109730 ↗
58. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexsNet-level accuracy with 50× fewer parameters and <0.5 MB model size. arXiv.
59. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
60. Euclidean distance matrices: Essential theory, algorithms, and applications10.1109/MSP.2015.2398954 ↗
61. Sauer, A., Schwarz, K., and Geiger, A. (2022, January 7–11). Stylegan-xl: Scaling stylegan to large diverse datasets. Proceedings of the ACM SIGGRAPH 2022 Conference Proceedings, Vancouver, BC, Canada.10.1145/3528233.3530738 ↗