A binocular vision method based on UNet image segmentation and RAFT-Stereo stereo matching for high-precision measurement of transparent liquid levels is proposed.To address the depth estimation errors caused by the lack of texture in transparent liquid scenarios

the method employs the UNet network to accurately segment the liquid surface

generating pixel-level masks to highlight liquid surface features.The RAFT-Stereo algorithm is then applied to compute disparity and convert it into depth information

thereby significantly improving the accuracy and robustness of liquid level measurement.Experimental results show that the proposed method outperforms the traditional semi-global block matching(SGBM)and RAFT-Stereo algorithms

reducing the mean absolute error(MAE)and maximum error(MaxE)by approximately 85.2% and 82.1%

respectively

and effectively mitigating issues such as blurry liquid surfaces and matching failures.Future research will further focus on optimizing the model’s real-time performance to provide reliable technical support for practical applications such as aerospace fuel tank monitoring.