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.

翻译：摘要：在日常生活与工业生产中，精确检测容器内液位变化至关重要。传统接触式测量方法存在一定局限性，而新兴的非接触式图像处理技术展现出良好的应用前景。本文提出了一种基于U^2-Net的容器动态液位检测模型。该模型利用SAM模型生成初始数据集，随后通过SemiReward框架评估并筛选出高质量的伪标签图像，构建专属数据集。模型采用U^2-Net从数据集中提取容器的掩膜图像，并运用形态学处理弥补掩膜缺陷。随后，模型计算相邻视频帧图像在同一位置处的灰度差值，通过设定差分阈值分割液位变化区域，最终利用轻量级神经网络对液位状态进行分类。该方法不仅缓解了复杂环境干扰的影响，还降低了对训练数据的需求，展现出较强的鲁棒性与泛化能力。大量实验结果表明，本文模型能够有效检测容器内液体的动态液位变化，为相关领域提供了一种新颖高效的解决方案。