Abstract
Noise which can appear in images affects the classification performance of Convolutional Neural Networks (CNNs). In particular, the impact of linear motion blur, which is one of the possible noises, in the classification performance of CNNs is assessed in this work. A realistic vision sensor model has been proposed to produce a linear motion blur effect in input images. This methodology allows analyzing how the performance of several considered state of the art CNNs is affected. Experiments that have been carried out indicate that the accuracy is heavily degraded by a high length of the displacement, while the angle of displacement deteriorates the performance to a lesser extent.
This work is partially supported by the following Spanish grants: TIN2016-75097-P, RTI2018-094645-B-I00 and UMA18-FEDERJA-084. All of them include funds from the European Regional Development Fund (ERDF). The authors acknowledge the funding from the Universidad de Málaga.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ciresan, D., Meier, U.: Multi-column deep neural networks for offline handwritten Chinese character classification. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (2013)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 4th edn. Pearson, New York (2018)
Gu, J., et al.: Recent advances in convolutional neural networks. arXiv:abs/1512.07108 (2015)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)
Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160, 106–54 (1962)
Russel, J.: Google’s Alphago AI wins three-match series against the world’s best go player. https://techcrunch.com/2017/05/24/alphago-beats-planets-best-human-go-player-kejie (2017)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
Molina-Cabello, M.A., Baena, R.M.L., López-Rubio, E., Thurnhofer-Hemsi, K.: Vehicle type detection by ensembles of convolutional neural networks operating on super resolved images. Integr. Comput.-Aided Eng. 25, 321–333 (2018)
PyTorch Contributors: PyTorch Documentation. Version 1.2.0 (2019). https://pytorch.org/docs/1.2.0/. Accessed 31 Oct 2019
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.: Mobilenetv 2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818–2826 (2016)
Szeliski, R.: Computer Vision: Algorithms and Applications, 1st edn. Springe, Heidelberg (2010)
Tan, M., et al.: MnasNet: Platform-aware neural architecture search for mobile. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2815–2823 (2019)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987–5995 (2017)
Zagoruyko, S., Komodakis, N.: Wide residual networks. In: Proceedings of the British Machine Vision Conference 2016, pp. 87.1–87.12. British Machine Vision Association, York (2016). https://doi.org/10.5244/C.30.87, http://www.bmva.org/bmvc/2016/papers/paper087/index.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Rodríguez-Rodríguez, J.A., Molina-Cabello, M.A., Benítez-Rochel, R., López-Rubio, E. (2021). The Impact of Linear Motion Blur on the Object Recognition Efficiency of Deep Convolutional Neural Networks. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-68780-9_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68779-3
Online ISBN: 978-3-030-68780-9
eBook Packages: Computer ScienceComputer Science (R0)
