Vision-based liquid level measurement offers advantages such as non-contact sensing

high accuracy

compact size

and general applicability across liquid types.However

its high cost and complex deployment requirements limit its widespread practical application.To address this problem

a transparent liquid level measurement method based on binocular stereo vision is proposed.An improved recurrent all-pairs field transforms-Stereo(RAFT-Stereo)network architecture is employed

which integrates a channel attention mechanism during feature extraction and introduces a region weighted loss function to enhance disparity accuracy in transparent and weakly textured regions.Experimental results show that the proposed method achieves an end-point error(EPE)of 0.53 and a Bad-3.0 of 9.61% on the SynClearDepth dataset.In the transparent liquid level height measurement experiment

the average measurement error is 1.68cm

which is about 78.3% lower than that of the original model

and can meet the accuracy requirements of practical liquid level measurement.