Research on Microporous Plate Liquid Level Detection Based on Machine Vision
The manual detection of liquid levels in porous microplate is time-consuming,laborious,and prone to inaccura-cies.To address this issue,a machine vision-based method for detecting liquid levels in microplate was proposed.This method in-volved the construction of a visual detection system by designing camera acquisition angles,lighting methods and so on to capture clear single-row liquid level images.Utilizing the principle of zero-mean normalized cross-correlation matching,an algorithm was developed to perform pose correction on median-filtered images and locate the positions of eight individual well liquid levels using centroid positioning of liquid level images.Liquid level detection was achieved by setting dual similarity thresholds,with irregular liquid level images undergoing secondary matching detection using masks.Experimental validation demonstrates that this method achieves a 100%accuracy in detecting liquid levels within a range of-10～10 μL of solution volume variation or more,meeting the accuracy requirements for microplate liquid level detection.
machine visionliquid level detectionimage processingmicroplatezero mean normalized cross-correlation matching
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