Deep learning-based algorithm for recognition of graduated cylinder in physical and chemical experiments
As pivotal instrument in physical and chemical experiments, graduated cylinders are typically used to dispense specific volume of liquid. Accurate readings of graduated cylinders is critical to the success of the experiment and the reliability of the data. However variations in liquid volume lead to...
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| Vydané v: | Proceedings (International Conference on Computer Engineering and Applications. Online) s. 1211 - 1216 |
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| Hlavný autor: | |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
12.04.2024
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| Predmet: | |
| ISSN: | 2159-1288 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | As pivotal instrument in physical and chemical experiments, graduated cylinders are typically used to dispense specific volume of liquid. Accurate readings of graduated cylinders is critical to the success of the experiment and the reliability of the data. However variations in liquid volume lead to the effective regions of the liquid levels and scales manifest as curves or semi-ellipses with distinct curvatures. This paper introduces an image intelligence algorithm with graduated cylinders as primary subject of study. It employs a combination of YOLOv8 and U-Net models to address the intricate challenge of precisely recognizing graduated cylinder metrology in physical and chemical experiments. The verification results show that the average accuracy of the cylinder readings output by the algorithm reaches 0.3% of the total range. It also performs in scale and liquid level recognition within complex scenes commendably and the accuracy rate reached 98.95% within a 1.5 milliliter margin, 96.34% within a 1 milliliter margin and 93.73% within a 0.5 milliliter margin. |
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| ISSN: | 2159-1288 |
| DOI: | 10.1109/ICCEA62105.2024.10604242 |