  In daily life and industrial production, it is crucial to accurately detect
changes in liquid level in containers. Traditional contact measurement methods
have some limitations, while emerging non-contact image processing technology
shows good application prospects. This paper proposes a container dynamic
liquid level detection model based on U^2-Net. This model uses the SAM model to
generate an initial data set, and then evaluates and filters out high-quality
pseudo-label images through the SemiReward framework to build an exclusive data
set. The model uses U^2-Net to extract mask images of containers from the data
set, and uses morphological processing to compensate for mask defects.
Subsequently, the model calculates the grayscale difference between adjacent
video frame images at the same position, segments the liquid level change area
by setting a difference threshold, and finally uses a lightweight neural
network to classify the liquid level state. This approach not only mitigates
the impact of intricate surroundings, but also reduces the demand for training
data, showing strong robustness and versatility. A large number of experimental
results show that the proposed model can effectively detect the dynamic liquid
level changes of the liquid in the container, providing a novel and efficient
solution for related fields.
