Level Measurements with Computer vision - Comparison of traditional and modern Computer vision Methods
Keywords:level measurement, computer vision, image segmentation, ResNet34
AbstractIn this work, modern machine learning methods are compared against traditional image processing techniques, for the purpose of estimating the level of coffee beans in a transparent tank ﬁtted to a coffee machine. Measurements using both approaches are compared against manual level measurements. The resulting algorithm are analysed for repeatability under scene variations, such as orientation of the tank with respect to the camera and the distribution of coffee beans.
John P Bentley. Principles of measurement systems. Pearson education, 2005.
Stevo Bozinovski. Reminder of the ﬁrst paper on transfer learning in neural networks, 1976. Informatica, 44(3), 2020.
Stevoand Bozinovski and Ante Fulgosi. The inﬂuence of pattern similarity and transfer learning upon the training of a base perceptron b2. Proceedings of Symposium Informatica, (3-121-5), 1976.
Gary Bradski and Adrian Kaehler. Learning OpenCV: Computer vision with the OpenCV library. " O’Reilly Media, Inc.", 2008.
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, 2009. doi:10.1109/CVPR.2009.5206848.
Sagi Eppel and Tal Kachman. Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications. arXiv preprint arXiv:1404.7174, 2014.
Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning, volume 1. MIT press Cambridge, 2016.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
Jeremy Howard and Sylvain Gugger. Fastai: A layered api for deep learning. Information, 11(2):108, Feb 2020. ISSN 2078-2489. doi:10.3390/info11020108.
Max Kuhn and Kjell Johnson. Applied predictive modeling, volume 26. Springer, 2013.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703, 2019.
Tara Zepel, Veronica Lai, Lars PE Yunker, and Jason E Hein. Automated liquid-level monitoring and control using computer vision. ChemRxiv Preprint, 10, 2020.
Copyright (c) 2022 Eirik Døble, Sindre Haugseter, Christian Mikkelsen, Jørgen Bang Sneisen, Nils-Olav Skeie, Ole Magnus Brastein
This work is licensed under a Creative Commons Attribution 4.0 International License.