High accuracy surface perceiving water level gauge with self calibration

Zheng, Gan; Zong, Hongyan

doi:10.1109/icma.2009.5246555

Cited by 1 publication

(1 citation statement)

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“…Manual reading depends on the observation of the water gauge by people, the real-time performance is poor and the subjective uncertainty and risk are greater in harsh environments such as high floods. Conventional automatic water level gauges are in contact with water or immersed in water, which can be divided into the float type and the pressure type according to the principle [4]. However, float-type gauges need to be installed in loggings and are expensive to construct.…”

Section: Introductionmentioning

confidence: 99%

A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology

Wu,

Huang,

Huang

et al. 2023

Water

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As pioneering non-contact water level measurement technologies, both computer vision and radar have effectively addressed challenges posed by traditional water level sensors in terms of maintenance cost, real-time responsiveness, and operational complexity. Moreover, they ensure high-precision measurements in appropriate conditions. These techniques can be seamlessly integrated into unmanned aerial vehicle (UAV) systems, significantly enhancing the spatiotemporal granularity of water level data. However, computer-vision-based water level measurement methods face the core problems of accurately identifying water level lines and elevation calculations, which can lead to measurement errors due to lighting variations and camera position offsets. Although deep learning has received much attention in improving the generation, the effectiveness of the models is limited by the diversity of the datasets. For the radar water level sensor, the hardware structure and signal processing algorithms have to be further improved. In the future, by constructing more comprehensive datasets, developing fast calibration algorithms, and implementing multi-sensor data fusion, it is expected that the robustness, accuracy, and computational efficiency of water level monitoring will be significantly improved, laying a solid foundation for further innovations and developments of hydrological monitoring.

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Section: Introductionmentioning

confidence: 99%