融合Transformer与残差通道注意力的恶劣场景水位智能检测方法

Water level intelligent detection method based on fuse Transformer residual channel attention mechanism in harsh environments

精准感知水位信息变化是实现精细水务管控和洪涝灾害的关键环节之一,而低照度、雾霾、雨雪、冰冻、波浪、镜头抖动等恶劣场景给水位检测带来极大挑战。针对现有方法中难以实现水位精准检测难题,构建一种融合Transformer与残差通道注意力机制的Unet模型(TRCAM-Unet),进而提出基于TRCAM-Unet的恶劣场景水位智能检测方法。关键技术包括通过全尺度连接结构实现多层次特征融合,通过Transformer模块强化区域特征的关联性,通过残差通道注意力模块强化有用信息的表达并削弱无用信息的干扰。相关试验和实践表明,TRCAM-Unet取得了98.84%MIOU评分与99.42%的MPA评分,在约150 m距离外水位检测最大误差不超过0.08 m,水位偏差均值(MLD)仅有1.609×10^(-2)m,优于Deeplab、PSPNet等主流语义分割算法。研究结果对解决恶劣场景下水位精准检测难题及洪涝灾害预警具有重要应用价值。

Accurate perception of water level changes is one of the key segments to achieve precision water affairs control and flood disaster,but harsh scenes such as low illumination,haze,rain and snow,freezing,lighting,and waves bring a great challenge to water level accurate detection.Aiming at the problem of accurate detection of water level in existing methods,this paper constructs a Unet model fused with transformer residual channel attention mechanism(called“TRCAM-Unet”),then,a water lever intelligent detection method in harsh environments based on TRCAM-Unet is proposed.The key technologies include that:Multi-level feature fusion is achieved by full scale connection structure.The relevance of regional feature is strengthened by transformer module.Strengthening the extraction ability of useful information and weakening the interference of useless information by residual channel attention module.The experiments and practices of water level semantic segmentation in harsh scenes shows that TRCAM-Unet achieved 98.84%MIOU scores and 99.42%MPA scores,the maximum error of water level detection outside 150 meters was not above 0.08 m,mean water level deviation(MLD)had only 1.609×10^(-2)meters,it is much better than the mainstream semantic segmentation models such as Deeplab,PSPNet,Unet.This study has important application value for water level accurate detection in harsh scenes and flood disaster early warning.