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| DOI：10.13522/j.cnki.ggps.2023459 |
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| Computer vision-based detection of liquid levels within cylindrical tanks in wastewater treatment |
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MA Huanhuan, LI Yunbei, ZHANG Yuanxin, HE Yu, SHI Ningning, FAN Anrui,
ZHOU Linhao, HUANG Biaobing, HONG Shuangxi, ZHANG Xiaoju
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1. School of Environment, Henan Normal University, Xinxiang 453007, China; 2. School of Software, Henan Normal University, Xinxiang 453007, China; 3. College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
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| Abstract: |
| 【Objective】Traditional methods for measuring specific resistance of sludge in wastewater treatment are limited by low efficiency, high errors and liquid surface oscillation. To address these issues, a computer vision-based intelligent monitoring system was developed to improves accuracy and automation of the measurements and overcome the limitations of conventional methods.【Method】A multi-modal image processing and intelligent monitoring platform was developed using OpenCV. Images of a graduated cylinder were converted to the HSV color images, followed by adaptive Region of Interest (ROI) cropping and morphological gradient operations to enhance the liquid surface edge detection. An improved bilateral filter was applied to remove the noise induced by bubbles and light reflections. The Otsu algorithm was employed for image segmentation, enabling precise recognition of liquid levels. The system was implemented using the Qt5 framework, incorporating multi-threaded video capture and dynamic compensation via a Kalman filter. Through a graphical user interface (GUI), the system provided liquid level visualization, remote linkage and real-time correction. This significantly improves accuracy and efficiency of dynamic liquid level monitoring and enhances system robustness.【Result】Experimental tests showed the absolute error of the system in liquid level measurement was less than 0.1 mL under standard conditions. Incorporating a deviation compensation can reduce the error to 0.01 mL, demonstrating the robustness and potential of the system for engineering application.【Conclusion】The intelligent detection system we developed overcomes the limitations of traditional dynamic liquid level measurement methods. Its modular architecture can be adapted for use in other fields, such as medical infusion monitoring and industrial process control. |
| Key words: image processing; cylinder liquid level; intelligent recognition; OpenCV |
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