Abstract
In order to solve the problem that the dust in the insulation cabinet is mistakenly identified as the liquid level in the process of automatic verification of the capillary viscometer, this paper studied the verification method of the capillary viscometer based on connected domain. On the basis of the common automatic verification system of capillary viscometer based on computer vision, the dust recognition method based on connected domain is added. Industrial cameras are used to acquire viscometer video images in real-time. In order to make the images clearer and improve the processing speed of the images, the following preprocessing is carried out on the acquired images first, including the ROI region selection, frame difference method to capture moving targets, binarization, corrosion, and expansion. Then, the preprocessed image is marked with connected domain. The parameter difference of the connected domain combining liquid level and dust, the problem of misidentified dust as liquid level is solved. The experimental results show that the time repeatability and constant reproducibility of the verification results of the proposed method are better than those of the ordinary verification method based on computer vision, which reduces the probability of misidentified dust as the liquid level and improves the accuracy and efficiency of the verification of capillary viscometer.
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Acknowledgements
This paper is supported by the Shandong Key Technology R&D Program 2019JZZY021005, Natural Science Foundation of Shandong ZR2020MF067, and Natural Science Foundation of Shandong Province ZR2021MF074.
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Jing, R. et al. (2024). Research on the Verification Method of Capillary Viscometer Based on Connected Domain. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_24
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DOI: https://doi.org/10.1007/978-3-031-50580-5_24
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