Computer vision-based methods of water level measurement that utilize cameras to capture and process images of water bodies and their surroundings are gaining attention due to their advantages over non-visual sensors. This study aims to improve the generalization ability of the water level measurement algorithm based on computer vision to promote the application of the method in a broader range of scenarios. First, we briefly introduce a pipeline consisting of two main steps: calibration and measurement. Second, we propose a novel cascade model that comprises global and local subnetworks to achieve a more precise waterline position coarse-to-fine. In the training phase, apart from basic data augmentation methods, we employ a multiscale training approach to utilize samples more effectively. Finally, compared with other methods, this study increases the accuracy rate and showcases superior accuracy, generalization ability, and application potential.

中文翻译：

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基于计算机视觉的梯级水位测量方法


基于计算机视觉的水位测量方法利用相机来捕获和处理水体及其周围环境的图像，由于其优于非视觉传感器而受到关注。本研究旨在提高基于计算机视觉的水位测量算法的泛化能力，以推动该方法在更广泛场景下的应用。首先，我们简要介绍一个由两个主要步骤组成的管道：校准和测量。其次，我们提出了一种新的级联模型，该模型由全局和局部子网组成，以实现更精确的从粗到细的水线位置。在训练阶段，除了基本的数据增强方法外，我们还采用多尺度训练方法来更有效地利用样本。最后，与其他方法相比，本研究提高了准确率，并展示了卓越的准确率、泛化能力和应用潜力。