Abstract
In this work, we present an efficient solution to the beer bottle cap classification problem. This problem arises in the Wecheer smart opener project. Although classification problem is common in Computer Vision, there is no dedicated work for beer bottle cap dataset. We combine state-of-the-art deep learning techniques to solve the problem. Our solution outperforms the well-known commercial system that is currently used by the Wecheer project. It is also more efficient than the famous architectures such as VGG, ResNet, and DenseNet for our purposes.
Supported by Kyanon Digital.
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
Volume of World Beer Production. European Beer Guide. Accessed 17 Oct 2006
Nelson, M.: The Barbarian’s Beverage: A History of Beer in Ancient Europe. Routledge, p. 1 (2005). ISBN 978-0-415-31121-2
Krizhevsky, A., Sutskever, I., Hinton, G.: Advances in neural information processing systems (2012)
Zeiler, M.D., Fergus, R.: Visualizing and Understanding Convolutional Networks. arXiv:1311.2901 (2013)
Zhang, X., Zou, J., He, K., et al.: Accelerating Very Deep Convolutional Networks for Classification and Detection. arXiv:1505.06798 (2015)
He, K., Zhang, X., Ren, S., et al.: Deep Residual Learning for Image Recognition. arXiv:1512.03385 (2015)
Huang, G., Liu, Z., van der Maaten, L., et al.: Densely Connected Convolutional Networks. arXiv:1608.06993 (2016)
https://docs.opencv.org/3.1.0/d5/daf/tutorial_py_histogram_equalization.html
Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv:1502.03167 (2015)
Zhou, B., Khosla, A., Lapedriza, A., et al.: Learning Deep Features for Discriminative Localization. arXiv:1512.04150 (2015)
Acknowledgement
We thank our colleagues, Hai Tran and Dac Dinh, for helpful discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Tran, Q.M., Nguyen, L.V., Huynh, T., Vo, H.H., Pham, V.T. (2019). Efficient CNN Models for Beer Bottle Cap Classification Problem. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-35653-8_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35652-1
Online ISBN: 978-3-030-35653-8
eBook Packages: Computer ScienceComputer Science (R0)