Abstract
Machine learning has proven to be an enormous asset in industrial settings time and time again. While these methods are responsible for some of the most impressive technical advancements in recent years, machine learning and in particular deep learning, still heavily rely on big datasets, containing all the necessary information to learn a particular task. However, the procurement of useful data often imposes costly adjustments in production in case of internal collection, or has copyright implications in case of external collection. In some cases, the collection fails due to insufficient data quality, or simply availability. Moreover, privacy can be an ethical as well as a legal concern. A promising approach that deals with all of these challenges is to artificially generate data. Unlike real-world data, purely synthetic data does not prompt privacy considerations, allows for better quality control, and in many cases the number of synthetic datapoints is theoretically unlimited. In this work, we explore the utility of synthetic data in industrial settings by outlining several use-cases in the field of Automatic Number Plate Recognition. In all cases synthetic data has the potential of improving the results of the respective deep learning algorithms, substantially reducing the time and effort of data acquisition and preprocessing, and eliminating privacy concerns in a field as sensitive as Automatic Number Plate Recognition.
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References
Cabon, Y., Murray, N., Humenberger, M.: Virtual kitti 2. arXiv preprint arXiv:2001.10773 (2020)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference On Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Gokaslan, A., Ramanujan, V., Ritchie, D., Kim, K.I., Tompkin, J.: Improving shape deformation in unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 662–678. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01258-8_40
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in neural information processing systems, vol. 27 (2014)
Han, B.G., Lee, J.T., Lim, K.T., Choi, D.H.: License plate image generation using generative adversarial networks for end-to-end license plate character recognition from a small set of real images. Appl. Sci. 10(8), 2780 (2020)
Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S.N., Rosaen, K., Vasudevan, R.: Driving in the matrix: can virtual worlds replace human-generated annotations for real world tasks?. arXiv preprint arXiv:1610.01983 (2016)
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110–8119 (2020)
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Tech. rep. (2009)
Laroca, R., et al.: A robust real-time automatic license plate recognition based on the yolo detector. In: 2018 International Joint Conference on Neural Networks (ijcnn), pp. 1–10. IEEE (2018)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Maltsev, A., Lebedev, R., Khanzhina, N.: On realistic generation of new format license plate on vehicle images. Proc. Comput. Sci. 193, 190–199 (2021)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Nikolenko, S.I.: Synthetic Data for Deep Learning. SOIA, vol. 174. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75178-4
Richter, S.R., Hayder, Z., Koltun, V.: Playing for benchmarks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2213–2222 (2017)
Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The synthia dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3234–3243 (2016)
Rosebrock, A.: Opencv: Automatic license/number plate recognition (anpr) with python (2015). https://pyimagesearch.com/2020/09/21/opencv-automatic-license-number-plate-recognition-anpr-with-python
Saleh, F.S., Aliakbarian, M.S., Salzmann, M., Petersson, L., Alvarez, J.M.: Effective use of synthetic data for urban scene semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 86–103. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_6
Sharma, M., Verma, A., Vig, L.: Learning to clean: a GAN perspective. In: Carneiro, G., You, S. (eds.) ACCV 2018. LNCS, vol. 11367, pp. 174–185. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21074-8_14
Tripathi, S., Chandra, S., Agrawal, A., Tyagi, A., Rehg, J.M., Chari, V.: Learning to generate synthetic data via compositing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 461–470 (2019)
Wang, X., Man, Z., You, M., Shen, C.: Adversarial generation of training examples: applications to moving vehicle license plate recognition. arXiv preprint arXiv:1707.03124 (2017)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
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Brunner, D., Schmid, F. (2022). Synthetic Data in Automatic Number Plate Recognition. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_11
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