联合上下文注意力机制的水位检测算法分析

Water level detection algorithm featured by a context attention mechanism

智能水位监测有助于及时的水资源管控和灾情防范。针对拍摄视角不同、恶劣天气和水面污染等问题,提出联合上下文注意力机制的水位检测算法,基于上下文注意力机制的UNet模型(CAM-UNet)和最小二乘多项式拟合函数,实现复杂背景下的水位信息远端智能获取。结果表明,在摄像头安装错位、镜头抖动及水面脏污等干扰造成水位定位困难的情况下,所提算法能够准确分割水位线,并在不依赖于水尺装置的情况下,将水位像素高度低偏差映射到实际高度,测定保证率和最大偏差符合《水位观测标准》。研究结果对解决复杂监控场景中的实时水位准确检测难题及洪涝预警具有重要应用价值。

Intelligent monitoring of water level plays a crucial role in timely water resource management and disaster prevention.To tackle challenges like varying shooting perspectives,adverse weather conditions,and water pollution,a water level detection algorithm incorporating a joint context attention mechanism was proposed.This algorithm,based on the context attention mechanism of the UNet model(CAMUNet)and the least squares polynomial fitting function,facilitated intelligent remote acquisition of water level information into intricate backgrounds.The research results demonstrated that the proposed algorithm could accurately segment water level lines even with amidst disturbances,such as misaligned camera installation,lens jitter and dirty water surfaces,the proposed algorithm accurately segmented the water level line.It achieved accurate without relying on water gauges by mapping the height deviations of water level pixels to real-world elevations,ensuring measurement assurance rates and maximum deviations in compliance with"Water Level Observation Standards".These research findings will hold significant application value in addressing the challenges of real-time precise water level detection as well as flood warning in complex monitoring scenarios.